Paper Digest: KDD 2023 Highlights
To search or review papers within KDD-2023 related to a specific topic, please use the search by venue and review by venue services. To browse papers by author, here is a list of top authors (KDD-2023). You may also like to explore our “Best Paper” Digest (KDD), which lists the most influential KDD papers since 1999.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is one of the top data mining conferences in the world. In 2023, it is to be held in Long Beach.
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TABLE 1: Paper Digest: KDD 2023 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Minimizing Hitting Time Between Disparate Groups with Shortcut Edges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we address the problem of adding a given number of shortcut edges in the network so as to directly minimize the average hitting time and the maximum hitting time between two disparate groups. |
Florian Adriaens; Honglian Wang; Aristides Gionis; |
| 2 | Maximizing Neutrality in News Ordering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the impact of the ordering of news stories on audience perception. |
Rishi Advani; Paolo Papotti; Abolfazl Asudeh; |
| 3 | Fair Allocation Over Time, with Applications to Content Moderation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the short-term planning problem of allocating human content reviewers to different harmful content categories. We use tools from fair division and study the application of competitive equilibrium and leximin allocation rules for addressing this problem. |
Amine Allouah; Christian Kroer; Xuan Zhang; Vashist Avadhanula; Nona Bohanon; Anil Dania; Caner Gocmen; Sergey Pupyrev; Parikshit Shah; Nicolas Stier-Moses; Ken Rodríguez Taarup; |
| 4 | LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to tackle textual noise by equipping cross-encoders with a novel LExical-aware Attention module (LEA) that incorporates lexical similarities between words in both sentences. |
Mario Almagro; Emilio Almazán; Diego Ortego; David Jiménez; |
| 5 | Rank-heterogeneous Preference Models for School Choice Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce two strategies for rank-heterogeneous choice modeling tailored for school choice. |
Amel Awadelkarim; Arjun Seshadri; Itai Ashlagi; Irene Lo; Johan Ugander; |
| 6 | Knowledge Graph Reasoning Over Entities and Numerical Values Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. |
Jiaxin Bai; Chen Luo; zheng li; Qingyu Yin; Bing Yin; Yangqiu Song; |
| 7 | Communication Efficient and Differentially Private Logistic Regression Under The Distributed Setting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the server tries to learn a model using the clients’ private datasets, the clients should provide each individual record in their local datasets with a formal privacy guarantee. Towards this end, we propose a general mechanism for logistic regression with DP under the distributed setting, based on output perturbation. |
Ergute Bao; Dawei Gao; Xiaokui Xiao; Yaliang Li; |
| 8 | Connecting The Dots — Density-Connectivity Distance Unifies DBSCAN, K-Center and Spectral Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reformulate DBSCAN through a clean objective function by introducing the density-connectivity distance (dc-dist), which captures the essence of density-based clusters by endowing the minimax distance with the concept of density. |
Anna Beer; Andrew Draganov; Ellen Hohma; Philipp Jahn; Christian M.M. Frey; Ira Assent; |
| 9 | Sketch-Based Anomaly Detection in Streaming Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time. |
Siddharth Bhatia; Mohit Wadhwa; Kenji Kawaguchi; Neil Shah; Philip S. Yu; Bryan Hooi; |
| 10 | Preemptive Detection of Fake Accounts on Social Networks Via Multi-Class Preferential Attachment Classifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we describe a new algorithm called Preferential Attac hment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. |
Adam Breuer; Nazanin Khosravani; Michael Tingley; Bradford Cottel; |
| 11 | On Improving The Cohesiveness of Graphs By Merging Nodes: Formulation, Analysis, and Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we study the problem of improving graph cohesiveness by merging nodes. |
Fanchen Bu; Kijung Shin; |
| 12 | MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, in this paper, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. |
Donghong Cai; Junru Chen; Yang Yang; Teng Liu; Yafeng Li; |
| 13 | When to Pre-Train Graph Neural Networks? From Data Generation Perspective! Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a generic framework W2PGNN to answer the crucial question of when to pre-train (. |
Yuxuan Cao; Jiarong Xu; Carl Yang; Jiaan Wang; Yunchao Zhang; Chunping Wang; Lei CHEN; Yang Yang; |
| 14 | Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we carefully design a customized encryption scheme named orthogonal matrix-based mask mechanism (O3M). |
Zeyu Cao; Zhipeng Liang; Bingzhe Wu; Shu Zhang; Hangyu Li; Ouyang Wen; Yu Rong; Peilin Zhao; |
| 15 | Efficient Coreset Selection with Cluster-based Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to significantly improve the efficiency of coreset selection while ensuring good effectiveness, by improving the SOTA approaches of using gradient descent without training machine learning models. |
Chengliang Chai; Jiayi Wang; Nan Tang; Ye Yuan; Jiabin Liu; Yuhao Deng; Guoren Wang; |
| 16 | SURE: Robust, Explainable, and Fair Classification Without Sensitive Attributes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So we may want the classifier to perform well on all subsets that are likely to be sensitive. We propose an iterative algorithm called SURE for this problem. |
Deepayan Chakrabarti; |
| 17 | Data-Efficient and Interpretable Tabular Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on two understudied aspects of AD that are critical for integration into real-world applications. |
Chun-Hao Chang; Jinsung Yoon; Sercan Ö. Arik; Madeleine Udell; Tomas Pfister; |
| 18 | IPOC: An Adaptive Interval Prediction Model Based on Online Chasing and Conformal Inference for Large-Scale Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address an online interval prediction problem (OnPred-Int) and adopt ensemble learning to solve it. |
Jiadong Chen; Yang Luo; Xiuqi Huang; Fuxin Jiang; Yangguang Shi; Tieying Zhang; Xiaofeng Gao; |
| 19 | On Hierarchical Disentanglement of Interactive Behaviors for Multimodal Spatiotemporal Data with Incompleteness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new framework, BiDisentMST, which leverages Gaussian Processes and Graph Factorization on the latent space to achieve our purposes. |
Jiayi Chen; Aidong Zhang; |
| 20 | Open-Set Semi-Supervised Text Classification with Latent Outlier Softening Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper extends STC to a more practical Open-set Semi-supervised Text Classification (OSTC) setting, which assumes that the unlabeled data contains out-of-distribution (OOD) texts. |
Junfan Chen; Richong Zhang; Junchi Chen; Chunming Hu; Yongyi Mao; |
| 21 | Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we strive to strengthen the representative capabilities of GNNs by devising a dedicated plug-and-play normalization scheme, termed as SUbgraph-sPEcific FactoR Embedded Normalization (SuperNorm), that explicitly considers the intra-connection information within each node-induced subgraph. |
Kaixuan Chen; Shunyu Liu; Tongtian Zhu; Ji Qiao; Yun Su; Yingjie Tian; Tongya Zheng; Haofei Zhang; Zunlei Feng; Jingwen Ye; Mingli Song; |
| 22 | Approximation Algorithms for Size-Constrained Non-Monotone Submodular Maximization in Deterministic Linear Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the problem of finding the maximum value of a non-negative submodular function subject to a limit on the number of items selected, a ubiquitous problem that appears in many applications, such as data summarization and nonlinear regression. |
Yixin Chen; Alan Kuhnle; |
| 23 | Accelerating Personalized PageRank Vector Computation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For undirected graphs, we propose two momentum-based acceleration methods that can be expressed as one-line updates and speed up non-acceleration methods by O (1 / √ α). |
Zhen Chen; Xingzhi Guo; Baojian Zhou; Deqing Yang; Steven Skiena; |
| 24 | Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. |
Zhijun Chen; Hailong Sun; Wanhao Zhang; Chunyi Xu; Qianren Mao; Pengpeng Chen; |
| 25 | Shilling Black-box Review-based Recommender Systems Through Fake Review Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. |
Hung-Yun Chiang; Yi-Syuan Chen; Yun-Zhu Song; Hong-Han Shuai; Jason S. Chang; |
| 26 | Classification of Edge-dependent Labels of Nodes in Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a classification of edge-dependent node labels as a new problem. |
Minyoung Choe; Sunwoo Kim; Jaemin Yoo; Kijung Shin; |
| 27 | Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a unified framework named HyNT that learns representations of a hyper-relational knowledge graph containing numeric literals in either triplets or qualifiers. |
Chanyoung Chung; Jaejun Lee; Joyce Jiyoung Whang; |
| 28 | Reducing Exposure to Harmful Content Via Graph Rewiring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we introduce Gamine, a fast greedy algorithm that can reduce the exposure to harmful content with or without quality constraints on recommendations. |
Corinna Coupette; Stefan Neumann; Aristides Gionis; |
| 29 | MGNN: Graph Neural Networks Inspired By Distance Geometry Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MetricGNN (MGNN), a spatial GNN model inspired by the congruent-insensitivity property of classifiers in the classification phase of GNNs. |
Guanyu Cui; Zhewei Wei; |
| 30 | Below The Surface: Summarizing Event Sequences with Generalized Sequential Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The resulting optimization problem does not lend itself for exact search, which is why we propose the heuristic Flock algorithm to efficiently find high-quality models in practice. |
Joscha Cüppers; Jilles Vreeken; |
| 31 | Deep Encoders with Auxiliary Parameters for Extreme Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper shows that in XC settings where data paucity and semantic gap issues abound, this can lead to suboptimal encoder training which negatively affects the performance of the overall architecture. |
Kunal Dahiya; Sachin Yadav; Sushant Sondhi; Deepak Saini; Sonu Mehta; Jian Jiao; Sumeet Agarwal; Purushottam Kar; Manik Varma; |
| 32 | A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though investigations have been made in conducting robust predictions and protecting membership privacy, they generally fail to simultaneously consider the robustness and membership privacy. Therefore, in this work, we study a novel problem of developing robust and membership privacy-preserving GNNs. |
Enyan Dai; Limeng Cui; Zhengyang Wang; Xianfeng Tang; Yinghan Wang; Monica Cheng; Bing Yin; Suhang Wang; |
| 33 | Contrastive Learning for User Sequence Representation in Personalized Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the search logs may be sparse or noisy in the real scenario, which is difficult for existing methods to learn accurate and robust user representations. To address this issue, we propose a contrastive learning framework CoPPS that aims to learn high-quality user representations for personalized product search. |
Shitong Dai; Jiongnan Liu; Zhicheng Dou; Haonan Wang; Lin Liu; Bo Long; Ji-Rong Wen; |
| 34 | Generalized Matrix Local Low Rank Representation By Random Projection and Submatrix Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for the general matrix local low rank representation problem. |
Pengtao Dang; Haiqi Zhu; Tingbo Guo; Changlin Wan; Tong Zhao; Paul Salama; Yijie Wang; Sha Cao; Chi Zhang; |
| 35 | TWIN: Personalized Clinical Trial Digital Twin Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a sample-efficient method TWIN for generating personalized clinical trial digital twins. |
Trisha Das; Zifeng Wang; Jimeng Sun; |
| 36 | Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we propose the Dynamic Adjacency Matrix Factorization (DAMF) algorithm, which achieves an efficient and accurate dynamic network embedding by rotating and scaling the coordinate system where the network embedding resides with no more than the number of edge modifications changes of node embeddings. |
Haoran Deng; Yang Yang; Jiahe Li; Haoyang Cai; Shiliang Pu; Weihao Jiang; |
| 37 | MetricPrompt: Prompting Model As A Relevance Metric for Few-shot Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose MetricPrompt, which eases verbalizer design difficulty by reformulating few-shot text classification task into text pair relevance estimation task. |
Hongyuan Dong; Weinan Zhang; Wanxiang Che; |
| 38 | Investigating Trojan Attacks on Pre-trained Language Model-powered Database Middleware Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We specifically propose a novel type of Trojan attack, where a maliciously designed PLM causes unexpected behavior in the database middleware. |
Peiran Dong; Song Guo; Junxiao Wang; |
| 39 | Localised Adaptive Spatial-Temporal Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we ask the following question: whether and to what extent can we localise spatial-temporal graph models? |
Wenying Duan; Xiaoxi He; Zimu Zhou; Lothar Thiele; Hong Rao; |
| 40 | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their memory and compute-intensive requirements pose a critical bottleneck for long-term forecasting, despite numerous advancements in compute-aware self-attention modules. To address this, we propose TSMixer, a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules. |
Vijay Ekambaram; Arindam Jati; Nam Nguyen; Phanwadee Sinthong; Jayant Kalagnanam; |
| 41 | Dependence and Model Selection in LLP: The Problem of Variants Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More fundamentally, we argue that a careful approach to model selection for LLP requires consideration of the dependence structure that exists between bags, items, and labels. In this paper we formalize this structure and show how it affects model selection. |
Gabriel Franco; Mark Crovella; Giovanni Comarela; |
| 42 | Multiplex Heterogeneous Graph Neural Network with Behavior Pattern Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, the over-smoothing issue of graph neural networks limits existing models to only capturing local structure signals but hardly learning the global relevant information of the network. To tackle these challenges, this work proposes a model called Behavior Pattern based Heterogeneous Graph Neural Network (BPHGNN) for multiplex heterogeneous network embedding. |
Chaofan Fu; Guanjie Zheng; Chao Huang; Yanwei Yu; Junyu Dong; |
| 43 | Delving Into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-turn Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, interpreting or visualizing the implicit knowledge acquired through self-supervised tasks proves challenging. In this study, we address these limitations by explicitly refining the knowledge required for response selection and structuring it into a coherent global flow, known as "dialogue structure." |
Tingchen Fu; Xueliang Zhao; Rui Yan; |
| 44 | Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By witnessing the success of pre-training models for protein modeling, in this paper, we develop the antibody pre-training language model and incorporate it into the antigen-specific antibody design model in a systemic way. |
Kaiyuan Gao; Lijun Wu; Jinhua Zhu; Tianbo Peng; Yingce Xia; Liang He; Shufang Xie; Tao Qin; Haiguang Liu; Kun He; Tie-Yan Liu; |
| 45 | Pyramid Graph Neural Network: A Graph Sampling and Filtering Approach for Multi-scale Disentangled Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a graph (signal) sampling and filtering framework, entitled Pyramid Graph Neural Network (PyGNN), which follows the Downsampling-Filtering-Upsampling-Decoding scheme. |
Haoyu Geng; Chao Chen; Yixuan He; Gang Zeng; Zhaobing Han; Hua Chai; Junchi Yan; |
| 46 | GAL-VNE: Solving The VNE Problem with Global Reinforcement Learning and Local One-Shot Neural Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a synergistic Global-And-Local learning approach for the VNE problem (GAL-VNE). |
Haoyu Geng; Runzhong Wang; Fei Wu; Junchi Yan; |
| 47 | Sparse Binary Transformers for Multivariate Time Series Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we apply sparse and binary-weighted Transformers to multivariate time series problems, showing that the lightweight models achieve accuracy comparable to that of dense floating-point Transformers of the same structure. |
Matt Gorbett; Hossein Shirazi; Indrakshi Ray; |
| 48 | 3D-Polishing for Triangular Mesh Compression of Point Cloud Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the polishing algorithm, we propose the approximate curvature radius to evaluate the scale of features polished at each iteration. |
Jiaqi Gu; Guosheng Yin; |
| 49 | ESSA: Explanation Iterative Supervision Via Saliency-guided Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, data augmentation on sophisticated data like medical images is particularly challenging due to the following: 1) scarcity of data in training the learning-based data augmenter, 2) difficulty in generating realistic and sophisticated images, and 3) difficulty in ensuring the augmented data indeed boosts the performance of explanation-guided learning. To solve these challenges, we propose an Explanation Iterative Supervision via Saliency-guided Data Augmentation (ESSA) framework for conducting explanation supervision and adversarial-trained image data augmentation via a synergized iterative loop that handles the translation from annotation to sophisticated images and the generation of synthetic image-annotation pairs with an alternating training strategy. |
Siyi Gu; Yifei Zhang; Yuyang Gao; Xiaofeng Yang; Liang Zhao; |
| 50 | CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end-to-end pipeline. |
Hangzhi Guo; Thanh H. Nguyen; Amulya Yadav; |
| 51 | SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel sketch, namely SketchPolymer to accurately estimate per-item tail quantile. |
Jiarui Guo; Yisen Hong; Yuhan Wu; Yunfei Liu; Tong Yang; Bin Cui; |
| 52 | On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the discoveries, we design JGCF, an efficient and effective method for CF based on Jacobi polynomial bases and frequency decomposition strategies. |
Jiayan Guo; Lun Du; Xu Chen; Xiaojun Ma; Qiang Fu; Shi Han; Dongmei Zhang; Yan Zhang; |
| 53 | Clenshaw Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce ClenshawGCN, a GNN model that injects the characteristic of spectral models into spatial models by a simple residual connection submodule: the Clenshaw residual connection, which is essentially a second-order negative residual combined with an initial residual. |
Yuhe Guo; Zhewei Wei; |
| 54 | CampER: An Effective Framework for Privacy-Aware Deep Entity Resolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the self-constructed training pairs, we present a collaborative fine-tuning approach to learn the match-aware and uni-space individual tuple embeddings for accurate matching decisions. |
Yuxiang Guo; Lu Chen; Zhengjie Zhou; Baihua Zheng; Ziquan Fang; Zhikun Zhang; Yuren Mao; Yunjun Gao; |
| 55 | A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Several recent works begin to study graph OOD detection, but they all need to train a graph neural network (GNN) from scratch with high computational cost. In this work, we make the first attempt to endow a well-trained GNN with the OOD detection ability without modifying its parameters. |
Yuxin Guo; Cheng Yang; Yuluo Chen; Jixi Liu; Chuan Shi; Junping Du; |
| 56 | Frigate: Frugal Spatio-temporal Forecasting on Road Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Connectivity within networks change due to road closures, constructions of new roads, etc. In this work, we develop Frigate to address all these shortcomings. |
Mridul Gupta; Hariprasad Kodamana; Sayan Ranu; |
| 57 | Detecting Interference in Online Controlled Experiments with Increasing Allocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a widely applicable procedure to test for interference in A/B testing with increasing allocation. |
Kevin Han; Shuangning Li; Jialiang Mao; Han Wu; |
| 58 | Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this risk, a time-delay is introduced in the implementation of control actions, but usually has a negative impact on the overall efficacy of the control policy. To address this challenge, this paper presents a novel Traffic Signal Control Framework (PRLight), which leverages an On-policy Traffic Control Model (OTCM) and an Online Traffic Prediction Model (OTPM) to achieve efficient and real-time control of traffic signals. |
Xiao Han; Xiangyu Zhao; Liang Zhang; Wanyu Wang; |
| 59 | GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach named Graph Attention Mean Field (GAT-MF). |
Qianyue Hao; Wenzhen Huang; Tao Feng; Jian Yuan; Yong Li; |
| 60 | CLUR: Uncertainty Estimation for Few-Shot Text Classification with Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Contrastive Learning from Uncertainty Relations (CLUR) to address UEFTC. |
Jianfeng He; Xuchao Zhang; Shuo Lei; Abdulaziz Alhamadani; Fanglan Chen; Bei Xiao; Chang-Tien Lu; |
| 61 | Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider the alignment between an upstream dimensionality reduction task of learning a low-dimensional representation of a set of high-dimensional data and a downstream optimization task of solving a stochastic program parameterized by said representation. |
Long He; Ho-Yin Mak; |
| 62 | Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we term the examples whose true label is outside the candidate label set OOC (Out-Of-Candidate) examples, and pioneer a new PLL study to learn with OOC examples. |
Shuo He; Lei Feng; Guowu Yang; |
| 63 | Planning to Fairly Allocate: Probabilistic Fairness in The Restless Bandit Setting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We thus introduce ProbFair, a probabilistically fair policy that maximizes total expected reward and satisfies the budget constraint while ensuring a strictly positive lower bound on the probability of being pulled at each timestep. |
Christine Herlihy; Aviva Prins; Aravind Srinivasan; John P. Dickerson; |
| 64 | Unbiased Locally Private Estimator for Polynomials of Laplacian Variables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a mechanism to debias polynomial functions computed from locally differentially private data. |
Quentin Hillebrand; Vorapong Suppakitpaisarn; Tetsuo Shibuya; |
| 65 | Graph Neural Processes for Spatio-Temporal Extrapolation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. |
Junfeng Hu; Yuxuan Liang; Zhencheng Fan; Hongyang Chen; Yu Zheng; Roger Zimmermann; |
| 66 | ST-iFGSM: Enhancing Robustness of Human Mobility Signature Identification Model Via Spatial-Temporal Iterative FGSM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though Deep neural networks (DNN) based HuMID models on spatial-temporal mobility fingerprint similarity demonstrate remarkable performance in effectively identifying human agents’ mobility signatures, it is vulnerable to adversarial attacks as other DNN-based models. Therefore, in this paper, we propose a Spatial-Temporal iterative Fast Gradient Sign Method with L0 regularization – ST-iFGSM – to detect the vulnerability and enhance the robustness of HuMID models. |
Mingzhi Hu; Xin Zhang; Yanhua Li; Xun Zhou; Jun Luo; |
| 67 | Leveraging Relational Graph Neural Network for Transductive Model Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional methods of pre-training, fine-tuning, and ensembling often overlook essential relational data and task interconnections. To address this gap, our study presents a novel approach to harnessing this relational information via a relational graph-based model. |
Zhengyu Hu; Jieyu Zhang; Haonan Wang; Siwei Liu; Shangsong Liang; |
| 68 | One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. |
Shaoyuan Huang; Zheng Wang; Heng Zhang; Xiaofei Wang; Cheng Zhang; Wenyu Wang; |
| 69 | Generalizing Graph ODE for Learning Complex System Dynamics Across Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we present GG-ODE (Generalized Graph Ordinary Differential Equations), a machine learning framework for learning continuous multi-agent system dynamics across environments. |
Zijie Huang; Yizhou Sun; Wei Wang; |
| 70 | The Information Pathways Hypothesis: Transformers Are Dynamic Self-Ensembles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the dynamic (i.e., input-dependent) nature of these pathways makes it difficult to prune dense self-attention during training. But the overall distribution of these pathways is often predictable. We take advantage of this fact to propose Stochastically Subsampled self-Attention (SSA) – a general-purpose training strategy for transformers that can reduce both the memory and computational cost of self-attention by 4 to 8 times during training while also serving as a regularization method – improving generalization over dense training. We show that an ensemble of sub-models can be formed from the subsampled pathways within a network, which can achieve better performance than its densely attended counterpart. |
Md Shamim Hussain; Mohammed J. Zaki; Dharmashankar Subramanian; |
| 71 | Sequential Learning Algorithms for Contextual Model-Free Influence Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the algorithm LSVI-GT-UCB, which implements the optimism in the face of uncertainty principle for episodic reinforcement learning with linear approximation. |
Alexandra Iacob; Bogdan Cautis; Silviu Maniu; |
| 72 | COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce two improvements to current MoE approaches. |
Shibal Ibrahim; Wenyu Chen; Hussein Hazimeh; Natalia Ponomareva; Zhe Zhao; Rahul Mazumder; |
| 73 | Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a variational Bayes algorithm for deriving the distributions of per variable attribution scores. |
Tsuyoshi Idé; Naoki Abe; |
| 74 | Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs Using An Adaptive Time Series Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel method to discover "warped" motifs whose lengths may differ. |
Makoto Imamura; Takaaki Nakamura; |
| 75 | Similarity Preserving Adversarial Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a similarity-preserving adversarial graph contrastive learning (SP-AGCL) framework that contrasts the clean graph with two auxiliary views of different properties (i.e., the node similarity-preserving view and the adversarial view). |
Yeonjun In; Kanghoon Yoon; Chanyoung Park; |
| 76 | Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors – Algorithm and Application Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Dash, an efficient and accurate PARAFAC2 decomposition method working in the dual-way streaming setting. |
Jun-Gi Jang; Jeongyoung Lee; Yong-chan Park; U Kang; |
| 77 | Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The classical temporal cross-validation framework for hyperparameter optimization (HPO) often leads to poor test performance because of a possible mismatch between validation and test periods. To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets. |
Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami; |
| 78 | Precursor-of-Anomaly Detection for Irregular Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. |
Sheo Yon Jhin; Jaehoon Lee; Noseong Park; |
| 79 | Community-based Dynamic Graph Learning for Popularity Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In that light, this paper develops a community-based dynamic graph learning method for popularity prediction. |
Shuo Ji; Xiaodong Lu; Mingzhe Liu; Leilei Sun; Chuanren Liu; Bowen Du; Hui Xiong; |
| 80 | GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GetPt, a unified pre-training architecture for general table representation applicable even to tables with complex structures and layouts. |
Ran Jia; Haoming Guo; Xiaoyuan Jin; Chao Yan; Lun Du; Xiaojun Ma; Tamara Stankovic; Marko Lozajic; Goran Zoranovic; Igor Ilic; Shi Han; Dongmei Zhang; |
| 81 | Enhancing Node-Level Adversarial Defenses By Lipschitz Regularization of Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend the Lipschitz analysis to graphs by providing a systematic scheme for estimating upper bounds of the Lipschitz constants of GNNs. |
Yaning Jia; Dongmian Zou; Hongfei Wang; Hai Jin; |
| 82 | Semantic Dissimilarity Guided Locality Preserving Projections for Partial Label Dimensionality Reduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works on partial label dimensionality reduction only exploit the disambiguated labels, but overlook the available semantic dissimilarity relationship hidden in the disambiguated labeling confidence, i.e., the smaller the inner product of the labeling confidences of two instances, the less likely they have the same ground-truth label. By combining such global dissimilarity relationship with local neighborhood information, we propose a novel partial label dimensionality reduction method named SDLPP, which employs an alternating procedure including candidate label disambiguation, semantic dissimilarity generation and dimensionality reduction. |
Yuheng Jia; Jiahao Jiang; Yongheng Wang; |
| 83 | Complementary Classifier Induced Partial Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional PLL classifier, to eliminate the false-positive labels in the candidate label set. |
Yuheng Jia; Chongjie Si; Min-Ling Zhang; |
| 84 | Anomaly Detection with Score Distribution Discrimination Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to optimize the anomaly scoring function from the view of score distribution, thus better retaining the diversity and more fine-grained information of input data, especially when the unlabeled data contains anomaly noises in more practical AD scenarios. |
Minqi Jiang; Songqiao Han; Hailiang Huang; |
| 85 | CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing studies of causal inference over time rely on the assumption that units are mutually independent, which is not valid for multi-agent dynamical systems. In this paper, we aim to bridge this gap and study how to estimate counterfactual outcomes in multi-agent dynamical systems. |
Song Jiang; Zijie Huang; Xiao Luo; Yizhou Sun; |
| 86 | FedSkill: Privacy Preserved Interpretable Skill Learning Via Imitation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present a privacy-preserved interpretable skill learning framework (FedSkill) that enables global policy learning to incorporate data from different sources and provides explainable interpretations to each local user without violating privacy and data sovereignty. |
Yushan Jiang; Wenchao Yu; Dongjin Song; Lu Wang; Wei Cheng; Haifeng Chen; |
| 87 | Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. |
Bowen Jin; Yu Zhang; Qi Zhu; Jiawei Han; |
| 88 | Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities. |
Yilun Jin; Kai Chen; Qiang Yang; |
| 89 | Predicting Information Pathways Across Online Communities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we address CLIPP by collecting large-scale, multi-modal datasets to examine the diffusion of online YouTube videos on Reddit. |
Yiqiao Jin; Yeon-Chang Lee; Kartik Sharma; Meng Ye; Karan Sikka; Ajay Divakaran; Srijan Kumar; |
| 90 | When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This does not account for full forecast distributions being consistent with the hierarchy and leading to poorly calibrated forecasts. We close both these gaps and propose PROFHiT, a probabilistic hierarchical forecasting model that jointly models forecast distributions over the entire hierarchy. |
Harshavardhan Kamarthi; Lingkai Kong; Alexander Rodriguez; Chao Zhang; B. Aditya Prakash; |
| 91 | R-Mixup: Riemannian Mixup for Biological Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. |
Xuan Kan; Zimu Li; Hejie Cui; Yue Yu; Ran Xu; Shaojun Yu; Zilong Zhang; Ying Guo; Carl Yang; |
| 92 | Exploiting Relation-aware Attribute Representation Learning in Knowledge Graph Embedding for Numerical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Relation-AAware attribute representation learning-based Knowledge Graph Embedding method for numerical reasoning tasks, which we call RAKGE. |
Gayeong Kim; Sookyung Kim; Ko Keun Kim; Suchan Park; Heesoo Jung; Hogun Park; |
| 93 | Efficient Distributed Approximate K-Nearest Neighbor Graph Construction By Multiway Random Division Forest Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MRDF (Multiway Random Division Forest), a scalable distributed algorithm that constructs highly accurate k-NN graph from numerous high-dimensional vectors quickly. |
Sang-Hong Kim; Ha-Myung Park; |
| 94 | Task Relation-aware Continual User Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel continual user representation learning method, called TERACON, whose learning capability is not limited as the number of learned tasks increases while capturing the relationship between the tasks. |
Sein Kim; Namkyeong Lee; Donghyun Kim; Minchul Yang; Chanyoung Park; |
| 95 | Task-Equivariant Graph Few-shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous episodic meta-learning based methods have demonstrated success in few-shot node classification, but our findings suggest that optimal performance can only be achieved with a substantial amount of diverse training meta-tasks. To address this challenge of meta-learning based few-shot learning (FSL), we propose a new approach, the Task-Equivariant Graph few-shot learning (TEG) framework. |
Sungwon Kim; Junseok Lee; Namkyeong Lee; Wonjoong Kim; Seungyoon Choi; Chanyoung Park; |
| 96 | How Transitive Are Real-World Group Interactions? – Measurement and Reproduction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the transitivity of group interactions in real-world hypergraphs. |
Sunwoo Kim; Fanchen Bu; Minyoung Choe; Jaemin Yoo; Kijung Shin; |
| 97 | LATTE: A Framework for Learning Item-Features to Make A Domain-Expert for Effective Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework, named as LATTE, to pre-train each core module in CRS (i.e., the recommendation and the conversation module) through abundant external data. |
Taeho Kim; Juwon Yu; Won-Yong Shin; Hyunyoung Lee; Ji-hui Im; Sang-Wook Kim; |
| 98 | Off-Policy Evaluation of Ranking Policies Under Diverse User Behavior Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While these estimators are somewhat effective in reducing the variance, all existing estimators apply a single universal assumption to every user, causing excessive bias and variance. Therefore, this work explores a far more general formulation where user behavior is diverse and can vary depending on the user context. |
Haruka Kiyohara; Masatoshi Uehara; Yusuke Narita; Nobuyuki Shimizu; Yasuo Yamamoto; Yuta Saito; |
| 99 | Deception By Omission: Using Adversarial Missingness to Poison Causal Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, when the data can be audited for correctness (e.g., it is cryptographically signed by its source), this adversarial mechanism is invalidated. This work introduces a novel attack methodology wherein the adversary deceptively omits a portion of the true training data to bias the learned causal structures in a desired manner (under strong signed sample input validation, this behavior seems to be the only strategy available to the adversary). |
Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung; |
| 100 | Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We build a model-based learning framework that infers a Markov Decision Process (MDP) from a dataset collected using a cyclic traffic signal control policy that is both commonplace and easy to gather. |
Mayuresh Kunjir; Sanjay Chawla; Siddarth Chandrasekar; Devika Jay; Balaraman Ravindran; |
| 101 | MM-DAG: Multi-task DAG Learning for Multi-modal Data – with Application for Traffic Congestion Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather systems, whose variables are multi-modal with scalars, vectors, and functions. |
Tian Lan; Ziyue Li; Zhishuai Li; Lei Bai; Man Li; Fugee Tsung; Wolfgang Ketter; Rui Zhao; Chen Zhang; |
| 102 | Shift-Robust Molecular Relational Learning with Causal Substructure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose CMRL that is robust to the distributional shift in molecular relational learning by detecting the core substructure that is causally related to chemical reactions. |
Namkyeong Lee; Kanghoon Yoon; Gyoung S. Na; Sein Kim; Chanyoung Park; |
| 103 | Boosting Multitask Learning on Graphs Through Higher-Order Task Affinities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the challenge, we develop an algorithm to cluster tasks into groups based on a higher-order task affinity measure. |
Dongyue Li; Haotian Ju; Aneesh Sharma; Hongyang R. Zhang; |
| 104 | Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate effective designs in Graph Neural Networks (GNNs) to sparsify brain graphs by eliminating noisy edges. |
Gaotang Li; Marlena Duda; Xiang Zhang; Danai Koutra; Yujun Yan; |
| 105 | Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". |
Haoxuan Li; Chunyuan Zheng; Peng Wu; Kun Kuang; Yue Liu; Peng Cui; |
| 106 | UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While promising, these aspect-planning methods struggle to generate specific information correctly, which prevents generated explanations from being convincing. In this paper, we claim that introducing lexical constraints can alleviate the above issues. |
Jiacheng Li; Zhankui He; Jingbo Shang; Julian McAuley; |
| 107 | Text Is All You Need: Learning Language Representations for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. |
Jiacheng Li; Ming Wang; Jin Li; Jinmiao Fu; Xin Shen; Jingbo Shang; Julian McAuley; |
| 108 | What’s Behind The Mask: Understanding Masked Graph Modeling for Graph Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. |
Jintang Li; Ruofan Wu; Wangbin Sun; Liang Chen; Sheng Tian; Liang Zhu; Changhua Meng; Zibin Zheng; Weiqiang Wang; |
| 109 | Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a constraint-aware and ranking-distilled token pruning method ToP, which selectively removes unnecessary tokens as input sequence passes through layers, allowing the model to improve online inference speed while preserving accuracy. |
Junyan Li; Li Lyna Zhang; Jiahang Xu; Yujing Wang; Shaoguang Yan; Yunqing Xia; Yuqing Yang; Ting Cao; Hao Sun; Weiwei Deng; Qi Zhang; Mao Yang; |
| 110 | Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general framework, namely Score-Weighted VCG, for designing learning-based ad auctions that account for externalities. |
Ningyuan Li; Yunxuan Ma; Yang Zhao; Zhijian Duan; Yurong Chen; Zhilin Zhang; Jian Xu; Bo Zheng; Xiaotie Deng; |
| 111 | OPORP: One Permutation + One Random Projection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: OPORP is a variant of the count-sketch data structure by using a fixed-length binning scheme and a normalization step for the estimation. |
Ping Li; Xiaoyun Li; |
| 112 | Multi-Temporal Relationship Inference in Urban Areas Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). |
Shuangli Li; Jingbo Zhou; Ji Liu; Tong Xu; Enhong Chen; Hui Xiong; |
| 113 | GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We are naturally inspired to enlarge the decision boundaries of minor classes and propose a general framework GraphSHA by Synthesizing HArder minor samples. |
Wen-Zhi Li; Chang-Dong Wang; Hui Xiong; Jian-Huang Lai; |
| 114 | HomoGCL: Rethinking Homophily in Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct a systematic analysis of this phenomenon and argue that homophily, i.e., the principle that "like attracts like”, plays a key role in the success of graph CL. Inspired to leverage this property explicitly, we propose HomoGCL, a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances. |
Wen-Zhi Li; Chang-Dong Wang; Hui Xiong; Jian-Huang Lai; |
| 115 | Learning Balanced Tree Indexes for Large-Scale Vector Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a lightweight learnable hierarchical space partitioning index based on a balanced K-ary tree, called BAlanced Tree Learner (BATL), where the same bucket of data points are represented by a path from the root to the corresponding leaf. |
Wuchao Li; Chao Feng; Defu Lian; Yuxin Xie; Haifeng Liu; Yong Ge; Enhong Chen; |
| 116 | Urban Region Representation Learning with OpenStreetMap Building Footprints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we utilize OSM buildings (footprints) complemented with points of interest (POIs) to learn region representations, as buildings’ shapes, spatial distributions, and properties have tight linkages to different urban functions. |
Yi Li; Weiming Huang; Gao Cong; Hao Wang; Zheng Wang; |
| 117 | Machine Unlearning in Gradient Boosting Decision Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel unlearning framework for GBDT. |
Huawei Lin; Jun Woo Chung; Yingjie Lao; Weijie Zhao; |
| 118 | MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a Model-agnostic Pretraining (MAP) framework that applies feature corruption and recovery on multi-field categorical data, and more specifically, we derive two practical algorithms: masked feature prediction (MFP) and replaced feature detection (RFD). |
Jianghao Lin; Yanru Qu; Wei Guo; Xinyi Dai; Ruiming Tang; Yong Yu; Weinan Zhang; |
| 119 | Fire: An Optimization Approach for Fast Interpretable Rule Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles. |
Brian Liu; Rahul Mazumder; |
| 120 | Communication Efficient Distributed Newton Method with Fast Convergence Rates Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a communication and computation efficient second-order method for distributed optimization. |
Chengchang Liu; Lesi Chen; Luo Luo; John C.S. Lui; |
| 121 | Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework for incorporating adversarial training into spatiotemporal traffic forecasting tasks. |
Fan Liu; Weijia Zhang; Hao Liu; |
| 122 | Discovering Dynamic Causal Space for DAG Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, measuring the data fitness alone regardless of DAG-ness inevitably leads to discovering suboptimal DAGs and model vulnerabilities. Towards this end, we propose a dynamic csusal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground-truth DAG. |
Fangfu Liu; Wenchang Ma; An Zhang; Xiang Wang; Yueqi Duan; Tat-Seng Chua; |
| 123 | Meta Multi-agent Exercise Recommendation: A Game Application Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, different groups of students (e.g., different countries, schools, or classes) have different solutions for the same group of KCs according to their own situations, in the spirit of competency-based instructing. Therefore, we propose Meta Multi-Agent Exercise Recommendation (MMER). |
Fei Liu; Xuegang Hu; Shuochen Liu; Chenyang Bu; Le Wu; |
| 124 | Semi-Supervised Graph Imbalanced Regression Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the lack of examples of rare label values in graph regression tasks, we propose a semi-supervised framework to progressively balance training data and reduce model bias via self-training. |
Gang Liu; Tong Zhao; Eric Inae; Tengfei Luo; Meng Jiang; |
| 125 | Enhancing Graph Representations Learning with Decorrelated Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We demonstrate that the propagation operator in GNNs exacerbates the feature correlation. In addition, we discovered through empirical study that existing decorrelation solutions fall short of maintaining a low feature correlation, potentially encoding redundant information. Thus, to more effectively address the over-correlation problem, we propose a decorrelated propagation scheme (DeProp) as a fundamental component to decorrelate the feature learning in GNN models, which achieves feature decorrelation at the propagation step. |
Hua Liu; Haoyu Han; Wei Jin; Xiaorui Liu; Hui Liu; |
| 126 | Guiding Mathematical Reasoning Via Mastering Commonsense Formula Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we establish FOMAS with two systems drawing insight from the dual process theory, including a Knowledge System and a Reasoning System, to learn and apply formula knowledge, respectively. The Knowledge System accumulates the math formulas, where we propose a novel pretraining manner to mimic how humans grasp the mathematical logic behind them. |
Jiayu Liu; Zhenya Huang; Zhiyuan Ma; Qi Liu; Enhong Chen; Tianhuang Su; Haifeng Liu; |
| 127 | B2-Sampling: Fusing Balanced and Biased Sampling for Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, those approaches usually suffer from two typical learning challenges. First, the number of candidate negative pairs is enormous. Thus, it is non-trivial to select representative ones to train the model in a more effective way. Second, the heuristics (e.g., graph views or meta-path patterns) to define positive and negative pairs are sometimes less reliable, causing considerable noise for both labelled” positive and negative pairs. In this work, we propose a novel sampling approach B2-Sampling to address the above challenges in a unified way. |
Mengyue Liu; Yun Lin; Jun Liu; Bohao Liu; Qinghua Zheng; Jin Song Dong; |
| 128 | Using Motif Transitions for Temporal Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we develop a practical temporal graph generator, Motif Transition Model (MTM), to generate synthetic temporal networks with realistic global and local features. |
Penghang Liu; Ahmet Erdem Sariyüce; |
| 129 | Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. |
Ruifeng Liu; Qu Liu; Tingjian Ge; |
| 130 | Generative Flow Network for Listwise Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality. |
Shuchang Liu; Qingpeng Cai; Zhankui He; Bowen Sun; Julian McAuley; Dong Zheng; Peng Jiang; Kun Gai; |
| 131 | Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we theoretically bridge degeneration with the predictor’s Lipschitz continuity. |
Wei Liu; Jun Wang; Haozhao Wang; Ruixuan Li; Yang Qiu; YuanKai Zhang; Jie Han; Yixiong Zou; |
| 132 | FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Confronting the distribution shift, a flexible encoder with refinement to the target distribution can generalize better on the test set than the stable invariant encoder. To remedy these weaknesses, we propose a Flexible invariant Learning framework for Out-Of-Distribution generalization on graphs (FLOOD), which comprises two key components, invariant learning and bootstrapped learning. |
Yang Liu; Xiang Ao; Fuli Feng; Yunshan Ma; Kuan Li; Tat-Seng Chua; Qing He; |
| 133 | Learning Strong Graph Neural Networks with Weak Information Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Accordingly, we propose D2PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities. |
Yixin Liu; Kaize Ding; Jianling Wang; Vincent Lee; Huan Liu; Shirui Pan; |
| 134 | QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the challenging but practically useful scenario, we propose a novel bankruptcy prediction model called the Quantity and Topology Imbalance-Aware Heterogeneous Graph Neural Network (QTIAH-GNN) to boost the final performance. |
Yucheng Liu; Zipeng Gao; Xiangyang Liu; Pengfei Luo; Yang Yang; Hui Xiong; |
| 135 | Multi-Grained Multimodal Interaction Network for Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context and implicit visual indication. Even worse, the inevitable noisy data may cause inconsistency of different modalities during the learning process, which severely degenerates the performance. To address the above issues, in this paper, we propose a novel Multi-GraIned Multimodal InteraCtion Network (MIMIC) framework for solving the MEL task. |
Pengfei Luo; Tong Xu; Shiwei Wu; Chen Zhu; Linli Xu; Enhong Chen; |
| 136 | Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed. To deal with this challenge, we propose a novel physics-guided learning method, which can not only encode observation knowledge such as initial and boundary conditions but also incorporate the basic physical principles and laws to guide the model optimization. |
Yingtao Luo; Qiang Liu; Yuntian Chen; Wenbo Hu; Tian Tian; Jun Zhu; |
| 137 | Augmenting Recurrent Graph Neural Networks with A Cache Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general approach for augmenting recurrent GNNs with a cache memory to improve their expressivity, especially for modeling long-range dependencies. |
Guixiang Ma; Vy A. Vo; Theodore L. Willke; Nesreen K. Ahmed; |
| 138 | Learning for Counterfactual Fairness from Observational Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE. |
Jing Ma; Ruocheng Guo; Aidong Zhang; Jundong Li; |
| 139 | Towards Graph-level Anomaly Detection Via Deep Evolutionary Mapping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although deep graph representation learning shows effectiveness in fusing high-level representations and capturing characters of individual graphs, most of the existing works are defective in graph-level anomaly detection because of their limited capability in exploring information across graphs, the imbalanced data distribution of anomalies, and low interpretability of the black-box graph neural networks (GNNs). To overcome these limitations, we propose a novel deep evolutionary graph mapping framework named GmapAD1, which can adaptively map each graph into a new feature space based on its similarity to a set of representative nodes chosen from the graph set. |
Xiaoxiao Ma; Jia Wu; Jian Yang; Quan Z. Sheng; |
| 140 | Context-aware Event Forecasting Via Graph Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. |
Yunshan Ma; Chenchen Ye; Zijian Wu; Xiang Wang; Yixin Cao; Tat-Seng Chua; |
| 141 | Querywise Fair Learning to Rank Through Multi-Objective Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous works in Fair LTR have attempted to resolve this by combining the objectives of relevant ranking and fair ranking into a single linear combination, but this approach is limited by the nonconvexity of the objective functions and can result in suboptimal relevance in ranking outputs. To address this, we propose a solution using Multi-Objective Optimization (MOO) algorithms. |
Debabrata Mahapatra; Chaosheng Dong; Michinari Momma; |
| 142 | Online Fairness Auditing Through Iterative Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such decision-making procedures are susceptible to intrinsic biases, which has led to a call for accountability in deployed decision systems. In this work, we investigate mechanisms that help audit claimed mathematical guarantees of the fairness of such systems. |
Pranav Maneriker; Codi Burley; Srinivasan Parthasarathy; |
| 143 | End-to-End Inventory Prediction and Contract Allocation for Guaranteed Delivery Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such a two-stage optimization often leads to inferior contract allocation performance. In this paper, our goal is to reduce this performance gap with a novel end-to-end approach. |
Wuyang Mao; Chuanren Liu; Yundu Huang; Zhonglin Zu; M Harshvardhan; Liang Wang; Bo Zheng; |
| 144 | Impatient Bandits: Optimizing Recommendations for The Long-Term Without Delay Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Increasingly, they are explicitly tasked with increasing users’ long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. |
Thomas M. McDonald; Lucas Maystre; Mounia Lalmas; Daniel Russo; Kamil Ciosek; |
| 145 | Hyper-USS: Answering Subset Query Over Multi-Attribute Data Stream Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Hyper-USS, an innovative sketching algorithm that supports subset query over multiple attributes accurately and efficiently. |
Ruijie Miao; Yiyao Zhang; Guanyu Qu; Kaicheng Yang; Tong Yang; Bin Cui; |
| 146 | Densest Diverse Subgraphs: How to Plan A Successful Cocktail Party with Diversity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on the problem of finding a densest diverse subgraph in a graph whose nodes have different attribute values/types that we refer to as colors. |
Atsushi Miyauchi; Tianyi Chen; Konstantinos Sotiropoulos; Charalampos E. Tsourakakis; |
| 147 | Learning to Relate to Previous Turns in Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new method to select relevant historical queries that are useful for the current query. |
Fengran Mo; Jian-Yun Nie; Kaiyu Huang; Kelong Mao; Yutao Zhu; Peng Li; Yang Liu; |
| 148 | Online Level-wise Hierarchical Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a family of new algorithms for online hierarchical clustering that combine high quality trees and fast per-point insertion time–made possible through a limited number of parallel non-greedy tree re-arrangements. |
Nicholas Monath; Manzil Zaheer; Andrew McCallum; |
| 149 | Causal Inference Via Style Transfer for Out-of-distribution Generalisation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method that effectively deals with hidden confounders by successfully implementing front-door adjustment (FA). |
Toan Nguyen; Kien Do; Duc Thanh Nguyen; Bao Duong; Thin Nguyen; |
| 150 | DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose DotHash, an unbiased estimator for the intersection size of two sets. |
Igor Nunes; Mike Heddes; Pere Vergés; Danny Abraham; Alex Veidenbaum; Alex Nicolau; Tony Givargis; |
| 151 | A Higher-Order Temporal H-Index for Evolving Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We analyze a recursive computation scheme and develop a highly scalable streaming algorithm. |
Lutz Oettershagen; Nils M. Kriege; Petra Mutzel; |
| 152 | Cracking White-box DNN Watermarks Via Invariant Neuron Transforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the first effective removal attack which cracks almost all the existing white-box watermarking schemes with provably no performance overhead and no required prior knowledge. |
Xudong Pan; Mi Zhang; Yifan Yan; Yining Wang; Min Yang; |
| 153 | Deep Weakly-supervised Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled. |
Guansong Pang; Chunhua Shen; Huidong Jin; Anton van den Hengel; |
| 154 | Criteria Tell You More Than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, rather than straightforwardly adopting existing GNN-based recommendation methods, we devise a novel criteria preference-aware light graph convolution (CPA-LGC ) method, which is capable of precisely capturing the criteria preference of users as well as the collaborative signal in complex high-order connectivities. |
Jin-Duk Park; Siqing Li; Xin Cao; Won-Yong Shin; |
| 155 | Domain-Guided Spatio-Temporal Self-Attention for Egocentric 3D Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera, make ego-HPE extremely challenging. To address these challenges, we propose a domain-guided spatio-temporal transformer model that leverages information specific to ego-views. |
Jinman Park; Kimathi Kaai; Saad Hossain; Norikatsu Sumi; Sirisha Rambhatla; Paul Fieguth; |
| 156 | FedDefender: Client-Side Attack-Tolerant Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models and avoid the adverse impact of malicious model updates from attackers, even when a server-side defense cannot identify or remove adversaries. |
Sungwon Park; Sungwon Han; Fangzhao Wu; Sundong Kim; Bin Zhu; Xing Xie; Meeyoung Cha; |
| 157 | Few-shot Low-resource Knowledge Graph Completion with Multi-view Task Representation Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the impact of the intractable long-tail problem on low-resource KG completion, in this paper, we propose a novel few-shot learning framework empowered by multi-view task representation generation. |
Shichao Pei; Ziyi Kou; Qiannan Zhang; Xiangliang Zhang; |
| 158 | Efficient Centrality Maximization with Rademacher Averages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we present CentRA, the first algorithm based on progressive sampling to compute high-quality approximations of the set of k most central nodes. |
Leonardo Pellegrina; |
| 159 | Locality Sensitive Hashing for Optimizing Subgraph Query Processing in Parallel Computing Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For interquery optimization, we propose a query locality sensitive hashing method named QMH, which can be used to detect common subgraphs among different subgraph queries, thereby merging multiple subgraph queries. |
Peng Peng; Shengyi Ji; Zhen Tian; Hongbo Jiang; Weiguo Zheng; Xuecang Zhang; |
| 160 | Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite being useful for several use cases, there is no work dedicated to learning from positive and unlabeled data in a multi-instance setting for anomaly detection. Therefore, we propose the first method that learns from PU bags in anomaly detection. |
Lorenzo Perini; Vincent Vercruyssen; Jesse Davis; |
| 161 | Deep Pipeline Embeddings for AutoML Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a remedy, this paper proposes a novel neural architecture that captures the deep interaction between the components of a Machine Learning pipeline. |
Sebastian Pineda Arango; Josif Grabocka; |
| 162 | Graph Neural Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by online recommendation scenarios, in this paper, we propose a framework named Graph Neural Bandits (GNB) to leverage the collaborative nature among users empowered by graph neural networks (GNNs). |
Yunzhe Qi; Yikun Ban; Jingrui He; |
| 163 | Towards Understanding and Enhancing Robustness of Deep Learning Models Against Malicious Unlearning Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we present a broad class of malicious unlearning attacks wherein maliciously crafted unlearning requests trigger deep learning models to misbehave on target samples in a highly controllable and predictable manner. |
Wei Qian; Chenxu Zhao; Wei Le; Meiyi Ma; Mengdi Huai; |
| 164 | Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel approach called Diverse and Discriminative representation Learning (DDLearn) for generalizable low-resource HAR. |
Xin Qin; Jindong Wang; Shuo Ma; Wang Lu; Yongchun Zhu; Xing Xie; Yiqiang Chen; |
| 165 | FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel FL framework, FedAPEN, which combines mutual learning and ensemble learning to take the advantages of private and shared global models while allowing heterogeneous models. |
Zhen Qin; Shuiguang Deng; Mingyu Zhao; Xueqiang Yan; |
| 166 | 3D-IDS: Doubly Disentangled Dynamic Intrusion Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although promising, our quantitative analysis shows that existing methods perform inconsistently in declaring various unknown attacks (e.g., 9% and 35% F1 respectively for two distinct unknown threats for an SVM-based method) or detecting diverse known attacks (e.g., 31% F1 for the Backdoor and 93% F1 for DDoS for a GCN-based state-of-the-art method), and reveals that the underlying cause is entangled distributions of flow features. This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme. |
Chenyang Qiu; Yingsheng Geng; Junrui Lu; Kaida Chen; Shitong Zhu; Ya Su; Guoshun Nan; Can Zhang; Junsong Fu; Qimei Cui; Xiaofeng Tao; |
| 167 | Reconstructing Graph Diffusion History from A Single Snapshot Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study an even harder problem, namely reconstructing Diffusion history from A single SnapsHot (DASH), where we seek to reconstruct the history from only the final snapshot without knowing true diffusion parameters. |
Ruizhong Qiu; Dingsu Wang; Lei Ying; H. Vincent Poor; Yifang Zhang; Hanghang Tong; |
| 168 | Source-Free Domain Adaptation with Temporal Imputation for Time Series Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance. To address this challenge, this paper presents a simple yet effective approach for source-free domain adaptation on time series data, namely MAsk and imPUte (MAPU). |
Mohamed Ragab; Emadeldeen Eldele; Min Wu; Chuan-Sheng Foo; Xiaoli Li; Zhenghua Chen; |
| 169 | FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although recent studies have adapted Cox Proportional Hazards (CoxPH) survival models for FSA, a systematic exploration of these challenges is currently lacking. In this paper, we address these critical challenges by introducing FedPseudo, a pseudo value-based deep learning framework for FSA. |
Md Mahmudur Rahman; Sanjay Purushotham; |
| 170 | Robustness Certification for Structured Prediction with General Inputs Via Safe Region Modeling in The Semimetric Output Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework of robustness certification for structured prediction problems, where the output space is modeled as a semimetric space with a distance function that satisfies non-negativity and symmetry but not necessarily the triangle inequality. |
Huaqing Shao; Lanjun Wang; Junchi Yan; |
| 171 | Temporal Dynamics-Aware Adversarial Attacks on Discrete-Time Dynamic Graph Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a novel adversarial attack on discrete-time dynamic graph models where we desire to perturb the input graph sequence in a manner that preserves the temporal dynamics of the graph while dropping the performance of representation learners. |
Kartik Sharma; Rakshit Trivedi; Rohit Sridhar; Srijan Kumar; |
| 172 | CARL-G: Clustering-Accelerated Representation Learning on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we ask: Can we borrow from classical unsupervised machine learning literature in order to overcome those obstacles? |
William Shiao; Uday Singh Saini; Yozen Liu; Tong Zhao; Neil Shah; Evangelos E. Papalexakis; |
| 173 | One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of one-shot joint extraction, registration and segmentation in neuroimaging data, which exploits only one labeled template image (a.k.a. atlas) and a few unlabeled raw images for training. We propose a unified end-to-end framework, called JERS, to jointly optimize the extraction, registration and segmentation tasks, allowing feedback among them. |
Yao Su; Zhentian Qian; Lei Ma; Lifang He; Xiangnan Kong; |
| 174 | Learning Autoregressive Model in LSM-Tree Based Store Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to offline learn the AR models locally in each page on incomplete data, and online aggregate the stored models in different pages with the consideration of the aforesaid inserted and updated data points. |
Yunxiang Su; Wenxuan Ma; Shaoxu Song; |
| 175 | PSLOG: Pretraining with Search Logs for Document Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to comprehensively leverage four query-document relevance relations, including co-interaction and multi-hop relations, to pretrain ranking models in IR. |
Zhan Su; Zhicheng Dou; Yujia Zhou; Ziyuan Zhao; Ji-Rong Wen; |
| 176 | Enhance Diffusion to Improve Robust Generalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to address two predominant problems in AT. |
Jianhui Sun; Sanchit Sinha; Aidong Zhang; |
| 177 | ShapleyFL: Robust Federated Learning Based on Shapley Value Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to improve the robustness of FL, we propose a Shapley value-inspired adaptive weighting mechanism, which regards the FL training as sequential cooperative games and adjusts clients’ weights according to their contributions. We also develop a client sampling strategy based on importance sampling, which can reduce the communication cost by optimizing the variance of the global updates according to the weights of clients. |
Qiheng Sun; Xiang Li; Jiayao Zhang; Li Xiong; Weiran Liu; Jinfei Liu; Zhan Qin; Kui Ren; |
| 178 | Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up hierarchical trading strategy design workflow of successful trading companies. |
Shuo Sun; Xinrun Wang; Wanqi Xue; Xiaoxuan Lou; Bo An; |
| 179 | All in One: Multi-Task Prompting for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-task prompting method for graph models. |
Xiangguo Sun; Hong Cheng; Jia Li; Bo Liu; Jihong Guan; |
| 180 | Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the "joint pre-training and local re-training” framework for learning and applying multi-source knowledge graph (KG) embeddings. |
Zequn Sun; Jiacheng Huang; Jinghao Lin; Xiaozhou Xu; Qijin Chen; Wei Hu; |
| 181 | Causal Effect Estimation on Hierarchical Spatial Graph Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new problem of predicting treatment effects on time series outcomes from spatial graph data with a hierarchical structure. |
Koh Takeuchi; Ryo Nishida; Hisashi Kashima; Masaki Onishi; |
| 182 | PERT-GNN: Latency Prediction for Microservice-based Cloud-Native Applications Via Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose PERT-GNN, a generic graph neural network based framework to predict the end-to-end latency for microservice applications. |
Da Sun Handason Tam; Yang Liu; Huanle Xu; Siyue Xie; Wing Cheong Lau; |
| 183 | ExplainableFold: Understanding AlphaFold Prediction with Explainable AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents ExplainableFold (xFold), which is an Explainable AI framework for protein structure prediction. |
Juntao Tan; Yongfeng Zhang; |
| 184 | Virtual Node Tuning for Few-shot Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). |
Zhen Tan; Ruocheng Guo; Kaize Ding; Huan Liu; |
| 185 | Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we point out that domain heterogeneity mainly lies in variant features under the invariant learning framework. |
Yunze Tong; Junkun Yuan; Min Zhang; Didi Zhu; Keli Zhang; Fei Wu; Kun Kuang; |
| 186 | Adversaries with Limited Information in The Friedkin-Johnsen Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present approximation algorithms for detecting a small set of users who are highly influential for the disagreement and polarization in the network. |
Sijing Tu; Stefan Neumann; Aristides Gionis; |
| 187 | Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a feature-based learning framework that effectively handles the counterfactual constraints and contributes itself to the limited pool of private explanation models. |
Vy Vo; Trung Le; Van Nguyen; He Zhao; Edwin V. Bonilla; Gholamreza Haffari; Dinh Phung; |
| 188 | Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we move to investigate the challenge of traffic flow prediction on an expanding traffic network. |
Binwu Wang; Yudong Zhang; Xu Wang; Pengkun Wang; Zhengyang Zhou; Lei Bai; Yang Wang; |
| 189 | Financial Default Prediction Via Motif-preserving Graph Neural Network with Curriculum Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although recent efforts suggest that default prediction can be improved by social relations, they fail to capture the higher-order topology structure at the level of small subgraph patterns. In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higher-order structures from multi-view motif-based graphs for financial default prediction. |
Daixin Wang; Zhiqiang Zhang; Yeyu Zhao; Kai Huang; Yulin Kang; Jun Zhou; |
| 190 | Accelerating Antimicrobial Peptide Discovery with Latent Structure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). |
Danqing Wang; Zeyu Wen; Fei Ye; Lei Li; Hao Zhou; |
| 191 | Networked Time Series Imputation Via Position-aware Graph Enhanced Variational Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this paper, we propose a novel approach to overcome these limitations. |
Dingsu Wang; Yuchen Yan; Ruizhong Qiu; Yada Zhu; Kaiyu Guan; Andrew Margenot; Hanghang Tong; |
| 192 | Incremental Causal Graph Learning for Online Root Cause Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model. |
Dongjie Wang; Zhengzhang Chen; Yanjie Fu; Yanchi Liu; Haifeng Chen; |
| 193 | GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The previous methods ignore this relational information, resulting in the selection of sub-optimal test questions. To address these challenges, we propose a Graph-Enhanced Multi-Objective method for CAT (GMOCAT). |
Hangyu Wang; Ting Long; Liang Yin; Weinan Zhang; Wei Xia; Qichen Hong; Dingyin Xia; Ruiming Tang; Yong Yu; |
| 194 | Treatment Effect Estimation with Adjustment Feature Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, assuming no mediator variables exist, we consider a more general setting by allowing for the existence of post-treatment and post-outcome variables rather than instrumental and precision variables in observed covariates. |
Haotian Wang; Kun Kuang; Haoang Chi; Longqi Yang; Mingyang Geng; Wanrong Huang; Wenjing Yang; |
| 195 | LightToken: A Task and Model-agnostic Lightweight Token Embedding Framework for Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing efforts to compress token embedding usually require the introduction of customized compression architectures or the optimization of model compression processes for individual downstream tasks, limiting their applicability in both model and task dimensions. To overcome these limitations and adhere to the principle of "one-for-all", we propose a lightweight token embedding framework named LightToken, which is able to produce compressed token embedding in a task and model-agnostic fashion. |
Haoyu Wang; Ruirui Li; Haoming Jiang; Zhengyang Wang; Xianfeng Tang; Bin Bi; Monica Cheng; Bing Yin; Yaqing Wang; Tuo Zhao; Jing Gao; |
| 196 | Adversarial Constrained Bidding Via Minimax Regret Optimization with Causality-Aware Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead of relying on the i.i.d. assumption, our insight is to align the train distribution of environments with the potential test distribution meanwhile minimizing policy regret. Based on this insight, we propose a practical Minimax Regret Optimization (MiRO) approach that interleaves between a teacher finding adversarial environments for tutoring and a learner meta-learning its policy over the given distribution of environments. |
Haozhe Wang; Chao Du; Panyan Fang; LI He; Liang Wang; Bo Zheng; |
| 197 | Efficient and Effective Edge-wise Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These methods either require high computational costs in sampling random walks or yield severely compromised representation quality because of falling short of capturing high-order information between edges. To address these challenges, we present TER and AER, which generate high-quality edge representation vectors based on the graph structure surrounding edges and edge attributes, respectively. |
Hewen Wang; Renchi Yang; Keke Huang; Xiaokui Xiao; |
| 198 | PROSE: Graph Structure Learning Via Progressive Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that current graph structure learning methods still pay no regard to the status of nodes and just judge all of their connections simultaneously using a monotonous standard, which will lead to indeterminacy and instability in the optimization process. |
Huizhao Wang; Yao Fu; Tao Yu; Linghui Hu; Weihao Jiang; Shiliang Pu; |
| 199 | Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, task-specific explanation methods are incapable of explaining pretrained GNNs whose downstream tasks are usually inaccessible, not to mention giving explanations for the transferable knowledge in pretrained GNNs. Additionally, task-specific methods only consider target models’ output in the label space, which are coarse-grained and insufficient to reflect the model’s internal logic. To address these limitations, we consider a two-stage explanation strategy, i.e., explainers are first pretrained in a task-agnostic fashion in the representation space and then further fine-tuned in the task-specific label space and representation space jointly if downstream tasks are accessible. |
Jihong Wang; Minnan Luo; Jundong Li; Yun Lin; Yushun Dong; Jin Song Dong; Qinghua Zheng; |
| 200 | WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent years have witnessed the great success of deep learning methods, eg., CNN and RNN, in time series classification and forecasting, but heterogeneity as the very nature of time series has not yet been addressed adequately and remains the performance treadstone. In this light, we argue that the intra-sequence non-stationarity and inter-sequence asynchronism are two types of heterogeneities widely existed in multiple times series, and propose a hybrid attention network called WHEN as deep learning solution. |
Jingyuan Wang; Chen Yang; Xiaohan Jiang; Junjie Wu; |
| 201 | Federated Few-shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. |
Song Wang; Xingbo Fu; Kaize Ding; Chen Chen; Huiyuan Chen; Jundong Li; |
| 202 | Contrastive Meta-Learning for Few-shot Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. |
Song Wang; Zhen Tan; Huan Liu; Jundong Li; |
| 203 | Improving Conversational Recommendation Systems Via Counterfactual Data Simulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. |
Xiaolei Wang; Kun Zhou; Xinyu Tang; Wayne Xin Zhao; Fan Pan; Zhao Cao; Ji-Rong Wen; |
| 204 | An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we explicitly take the correlations between observed and missing values into account, and theoretically re-derive the Evidence Lower BOund (ELBO) of conditional diffusion model in the scenario of multivariate time series imputation. |
Xu Wang; Hongbo Zhang; Pengkun Wang; Yudong Zhang; Binwu Wang; Zhengyang Zhou; Yang Wang; |
| 205 | Automated 3D Pre-Training for Molecular Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. |
Xu Wang; Huan Zhao; Wei-wei Tu; Quanming Yao; |
| 206 | Efficient Sparse Linear Bandits Under High Dimensional Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a computationally efficient Lasso Random Project Bandit (LRP-Bandit) algorithm for sparse linear bandit problems under high-dimensional settings with limited samples. |
Xue Wang; Mike Mingcheng Wei; Tao Yao; |
| 207 | Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. |
Yangyang Wang; Xiao Zhang; Mingyi Li; Tian Lan; Huashan Chen; Hui Xiong; Xiuzhen Cheng; Dongxiao Yu; |
| 208 | Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. |
Yifan Wang; Peijie Sun; Min Zhang; Qinglin Jia; Jingjie Li; Shaoping Ma; |
| 209 | Rapid Image Labeling Via Neuro-Symbolic Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it is prohibitively expensive to annotate images in key domains such as healthcare, where data labeling requires significant domain expertise and cannot be easily delegated to crowd workers. To address this challenge, we propose a neuro-symbolic approach called RAPID, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules. |
Yifeng Wang; Zhi Tu; Yiwen Xiang; Shiyuan Zhou; Xiyuan Chen; Bingxuan Li; Tianyi Zhang; |
| 210 | Learning to Schedule in Diffusion Probabilistic Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the assumption that all instances should have the same schedule is not always valid. To address these problems, this paper proposes a method that leverages reinforcement learning to automatically search for an optimal sampling schedule for DPMs. |
Yunke Wang; Xiyu Wang; Anh-Dung Dinh; Bo Du; Charles Xu; |
| 211 | A Message Passing Neural Network Space for Better Capturing Data-dependent Receptive Fields Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods are limited to designing a common solution for different graphs, which fails to capture the impact of different graph properties on the receptive fields. To alleviate such issues, we propose a novel MPNN space for data-dependent receptive fields (MpnnDRF), which enables us to dynamically design suitable MPNNs to capture the receptive field for the given graph. |
Zhili Wang; Shimin Di; Lei Chen; |
| 212 | Efficient Bi-Level Optimization for Recommendation Denoising Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. |
Zongwei Wang; Min Gao; Wentao Li; Junliang Yu; Linxin Guo; Hongzhi Yin; |
| 213 | Meta Graph Learning for Long-tail Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We innovatively propose a novel Meta Graph Learning framework for long-tail recommendation (MGL) for solving both challenges. |
Chunyu Wei; Jian Liang; Di Liu; Zehui Dai; Mang Li; Fei Wang; |
| 214 | To Aggregate or Not? Learning with Separate Noisy Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The literature has also studied extensively on effective aggregation approaches. This paper revisits this choice and aims to provide an answer to the question of whether one should aggregate separate noisy labels into single ones or use them separately as given. |
Jiaheng Wei; Zhaowei Zhu; Tianyi Luo; Ehsan Amid; Abhishek Kumar; Yang Liu; |
| 215 | Granger Causal Chain Discovery for Sepsis-Associated Derangements Via Continuous-Time Hawkes Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a linear multivariate Hawkes process model, coupled with ReLU link function, to recover a Granger Causal (GC) graph with both exciting and inhibiting effects. |
Song Wei; Yao Xie; Christopher S. Josef; Rishikesan Kamaleswaran; |
| 216 | Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness hyperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. |
Yan Wen; Chen Gao; Lingling Yi; Liwei Qiu; Yaqing Wang; Yong Li; |
| 217 | Deep Bayesian Active Learning for Accelerating Stochastic Simulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. |
Dongxia Wu; Ruijia Niu; Matteo Chinazzi; Alessandro Vespignani; Yi-An Ma; Rose Yu; |
| 218 | Self-Adaptive Perturbation Radii for Adversarial Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this conflict, in this paper, firstly we show the superiority of adaptive perturbation radii on the accuracy and robustness respectively. Then we propose our novel self-adaptive adjustment framework for perturbation radii without tedious searching. |
Huimin Wu; Wanli Shi; Chenkang Zhang; Bin Gu; |
| 219 | DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we approach the fake news detection problem with a novel aspect of social graph refinement. |
Jiaying Wu; Bryan Hooi; |
| 220 | Personalized Federated Learning with Parameter Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We dive into personalized federated learning from the perspective of privacy-preserving transfer learning, and identify the limitations of previous personalized federated learning algorithms. |
Jun Wu; Wenxuan Bao; Elizabeth Ainsworth; Jingrui He; |
| 221 | Certified Edge Unlearning for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While retraining the model from scratch by excluding the specific edges can eliminate their influence, this approach incurs a high computational cost. To overcome this challenge, we introduce CEU, a Certified Edge Unlearning framework. |
Kun Wu; Jie Shen; Yue Ning; Ting Wang; Wendy Hui Wang; |
| 222 | Recognizing Unseen Objects Via Multimodal Intensive Knowledge Graph Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, we propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings via a designed dense attention module and self-calibration loss. |
Likang Wu; Zhi Li; Hongke Zhao; Zhefeng Wang; Qi Liu; Baoxing Huai; Nicholas Jing Yuan; Enhong Chen; |
| 223 | Towards Reliable Rare Category Analysis on Graphs Via Individual Calibration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the observation, we propose a novel individual calibration framework, named CALIRARE, for alleviating the unique challenges of RCA, thus enabling reliable rare category analysis. |
Longfeng Wu; Bowen Lei; Dongkuan Xu; Dawei Zhou; |
| 224 | TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Mechanism Via Gated Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we formulate TSC as a sequence modeling problem with a sequence of Markov decision process described by states, actions, and rewards from the traffic environment. |
Qiang Wu; Mingyuan Li; Jun Shen; Linyuan Lü; Bo Du; Ke Zhang; |
| 225 | Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For example, existing single-machine-based AUPRC maximization algorithms maintain an inner state for local each data point, thus these methods are not applicable to large-scale multi-party collaborative training due to the dependence on each local data point. To address the above challenge, in this paper, we reformulate the serverless multi-party collaborative AUPRC maximization problem as a conditional stochastic optimization problem in a serverless multi-party collaborative learning setting and propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC. |
Xidong Wu; Zhengmian Hu; Jian Pei; Heng Huang; |
| 226 | MicroscopeSketch: Accurate Sliding Estimation Using Adaptive Zooming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. |
Yuhan Wu; Shiqi Jiang; Siyuan Dong; Zheng Zhong; Jiale Chen; Yutong Hu; Tong Yang; Steve Uhlig; Bin Cui; |
| 227 | MedLink: De-Identified Patient Health Record Linkage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing record linkage approaches primarily rely on patient identifiers, which have inherent limitations such as privacy invasion and identifier discrepancies. To tackle this problem, we propose linking de-identified patient health records by matching health patterns without strictly relying on sensitive patient identifiers. |
Zhenbang Wu; Cao Xiao; Jimeng Sun; |
| 228 | A Sequence-to-Sequence Approach with Mixed Pointers to Topic Segmentation and Segment Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel Sequence-to-Sequence approach with Mixed Pointers (Seq2Seq-MP). |
Jinxiong Xia; Houfeng Wang; |
| 229 | User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There is also no effective way for traditional debiasing methods to measure potentially useful biases through conversational key-terms to enhance the recommendation performance. In this paper, we develop a deconfounded CRS, which enables the user to provide both item and key-term feedback in each round such that we can promisingly capture more accurate relation between key-term-level and item-level user preference to alleviate the bias. |
Yu Xia; Junda Wu; Tong Yu; Sungchul Kim; Ryan A. Rossi; Shuai Li; |
| 230 | Graph Contrastive Learning with Generative Adversarial Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. |
Cheng Wu; Chaokun Wang; Jingcao Xu; Ziyang Liu; Kai Zheng; Xiaowei Wang; Yang Song; Kun Gai; |
| 231 | A Causality Inspired Framework for Model Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a unified causal lens for understanding representative model interpretation methods. |
Chenwang Wu; Xiting Wang; Defu Lian; Xing Xie; Enhong Chen; |
| 232 | Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing anomaly detection models for time series are primarily trained with normal-point-dominant data and would become ineffective when anomalous points intensively occur in certain episodes. To solve this problem, we propose a new approach, called DiffAD, from the perspective of time series imputation. |
Chunjing Xiao; Zehua Gou; Wenxin Tai; Kunpeng Zhang; Fan Zhou; |
| 233 | Spatial Heterophily Aware Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, in this paper, we propose a metric, named Spatial Diversity Score, to quantitatively measure the spatial heterophily and show how it can influence the performance of GNNs. |
Congxi Xiao; Jingbo Zhou; Jizhou Huang; Tong Xu; Hui Xiong; |
| 234 | Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation. |
Teng Xiao; Zhengyu Chen; Suhang Wang; |
| 235 | A Dual-Agent Scheduler for Distributed Deep Learning Jobs on Public Cloud Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: First, most learning-based methods only focus on ordering or placement policy independently, while ignoring their cooperation. Second, the unbalanced machine performances and resource contention impose huge overhead and uncertainty on job duration, but rarely be considered in existing work. To tackle these issues, this paper presents a dual-agent scheduler framework abstracted from the two sub-tasks to jointly learn the ordering and placement policies and make better-informed scheduling decisions. |
Mingzhe Xing; Hangyu Mao; Shenglin Yin; Lichen Pan; Zhengchao Zhang; Zhen Xiao; Jieyi Long; |
| 236 | Learning Behavior-oriented Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Learning Behavior-oriented Knowledge Tracing (LBKT) model, with the goal of explicitly exploring the learning behavior effects on learners’ knowledge states. |
Bihan Xu; Zhenya Huang; Jiayu Liu; Shuanghong Shen; Qi Liu; Enhong Chen; Jinze Wu; Shijin Wang; |
| 237 | How Does The Memorization of Neural Networks Impact Adversarial Robust Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN’s accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN’s performance on typical samples. |
Han Xu; Xiaorui Liu; Wentao Wang; Zitao Liu; Anil K Jain; Jiliang Tang; |
| 238 | Grace: Graph Self-Distillation and Completion to Mitigate Degree-Related Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we point out that under-represented self-representations and low neighborhood homophily ratio of low-degree nodes are two main culprits. |
Hui Xu; Liyao Xiang; Femke Huang; Yuting Weng; Ruijie Xu; Xinbing Wang; Chenghu Zhou; |
| 239 | Internal Logical Induction for Pixel-Symbolic Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Tailoring the processing approach based on the properties of these two input types can contribute to solving the problem more effectively. To tackle the above issue, we propose an Internal Logical Induction (ILI) framework that integrates deep RL and rule learning into one system. |
Jiacheng Xu; Chao Chen; Fuxiang Zhang; Lei Yuan; Zongzhang Zhang; Yang Yu; |
| 240 | MimoSketch: A Framework to Mine Item Frequency on Multiple Nodes with Sketches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We design a framework named MimoSketch for the MIMO-specific scenario, which improves the fundamental mining task of item frequency estimation. |
Yuchen Xu; Wenfei Wu; Bohan Zhao; Tong Yang; Yikai Zhao; |
| 241 | Kernel Ridge Regression-Based Graph Dataset Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, inspired by the recent advances in neural network kernel methods, we adopt a kernel ridge regression-based meta-learning objective which has a feasible exact solution. |
Zhe Xu; Yuzhong Chen; Menghai Pan; Huiyuan Chen; Mahashweta Das; Hao Yang; Hanghang Tong; |
| 242 | Node Classification Beyond Homophily: Towards A General Solution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem from the structure learning perspective and propose a family of general solutions named ALT. |
Zhe Xu; Yuzhong Chen; Qinghai Zhou; Yuhang Wu; Menghai Pan; Hao Yang; Hanghang Tong; |
| 243 | PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the problem, we propose a novel paradigm, recommender systems with human preferences (or Preference-based Recommender systems), which allows RL recommender systems to learn from preferences about users’ historical behaviors rather than explicitly defined rewards. |
Wanqi Xue; Qingpeng Cai; Zhenghai Xue; Shuo Sun; Shuchang Liu; Dong Zheng; Peng Jiang; Kun Gai; Bo An; |
| 244 | E-commerce Search Via Content Collaborative Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their promising performances, they are (1) lacking proper semantic representation of product contents; (2) less efficient for industry-scale graphs; and (3) less accurate on long-tail queries and cold-start products. To address these problems simultaneously, this paper proposes CC-GNN, a novel Content Collaborative Graph Neural Network. |
Guipeng Xv; Chen Lin; Wanxian Guan; Jinping Gou; Xubin Li; Hongbo Deng; Jian Xu; Bo Zheng; |
| 245 | CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop CriticalFL, a CLP augmented FL framework to reveal that adaptively augmenting exiting FL methods with CLP, the resultant performance is significantly improved when the client selection is guided by the discovered CLP. |
Gang Yan; Hao Wang; Xu Yuan; Jian Li; |
| 246 | Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing general-purpose user representation researches have little ability for full-life cycle modeling on extremely long behavior sequences since user registration. In this study, we propose a novel framework called full- Life cycle User Representation Model (LURM) to tackle this challenge. |
Bei Yang; Jie Gu; Ke Liu; Xiaoxiao Xu; Renjun Xu; Qinghui Sun; Hong Liu; |
| 247 | Fragility Index: A New Approach for Binary Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel metric, the Fragility Index (FI), to evaluate the performance of binary classifiers by capturing the magnitude of the error. |
Chen Yang; Ziqiang Zhang; Bo Cao; Zheng Cui; Bin Hu; Tong Li; Daniel Zhuoyu Long; Jin Qi; Feng Wang; Ruohan Zhan; |
| 248 | IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To take a step towards easy-to-understand and reproducible research of scientific design, we propose a benchmark for the inverse design of nanophotonic devices, which can be verified computationally and accurately. |
Jia-Qi Yang; Yucheng Xu; Jia-Lei Shen; Kebin Fan; De-Chuan Zhan; Yang Yang; |
| 249 | MAPLE: Semi-Supervised Learning with Multi-Alignment and Pseudo-Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike previous methods, this paper focuses on the study of learning deep models by gathering knowledge from multiple sources in a labor-free fashion and further proposes the Multi-Alignment and Pseudo-Learning” method, dubbed MAPLE. |
Juncheng Yang; Chao Li; Zuchao Li; Wei Yu; Bo Du; Shijun Li; |
| 250 | EXTRACT and REFINE: Finding A Support Subgraph Set for Graph Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a unified subgraph learning scheme, Poly-Pivot Graph Neural Network (P2GNN) where we designate the centric node of each subgraph as the pivot. |
Kuo Yang; Zhengyang Zhou; Wei Sun; Pengkun Wang; Xu Wang; Yang Wang; |
| 251 | ΚHGCN: Tree-likeness Modeling Via Continuous and Discrete Curvature Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the end, a curvature-aware hyperbolic graph convolutional neural network, ?HGCN, is proposed, which utilizes the curvature to guide message passing and improve long-range propagation. |
Menglin Yang; Min Zhou; Lujia Pan; Irwin King; |
| 252 | Specify Robust Causal Representation from Mixed Observations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models. In this paper, we develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures, according to the hypothetical factored causal graph. |
Mengyue Yang; Xinyu Cai; Furui Liu; Weinan Zhang; Jun Wang; |
| 253 | Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel counterfactual learning method, named CF-HGExplainer, for heterogeneous graphs. |
Qiang Yang; Changsheng Ma; Qiannan Zhang; Xin Gao; Chuxu Zhang; Xiangliang Zhang; |
| 254 | LightPath: Lightweight and Scalable Path Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a lightweight and scalable path representation learning framework, termed LightPath, that aims to reduce resource consumption and achieve scalability without affecting accuracy, thus enabling broader applicability. |
Sean Bin Yang; Jilin Hu; Chenjuan Guo; Bin Yang; Christian S. Jensen; |
| 255 | Test Accuracy Vs. Generalization Gap: Model Selection in NLP Without Accessing Training or Testing Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we expand on prior analyses by examining generalization-metric-based model selection with the following objectives: (i) focusing on natural language processing (NLP) tasks, as prior work primarily concentrates on computer vision (CV) tasks; (ii) considering metrics that directly predict test error instead of the generalization gap; (iii) exploring metrics that do not need access to data to compute. |
Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney; |
| 256 | MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In fact, each token of a text contains different style intensity and makes different contribution to the overall style. Our proposed method addresses this issue by assigning individual style vector to each token in a text, allowing for fine-grained control and manipulation of the style strength. |
Yazheng Yang; Zhou Zhao; Qi Liu; |
| 257 | DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. |
Yiyuan Yang; Chaoli Zhang; Tian Zhou; Qingsong Wen; Liang Sun; |
| 258 | Knowledge Graph Self-Supervised Rationalization for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. |
Yuhao Yang; Chao Huang; Lianghao Xia; Chunzhen Huang; |
| 259 | BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to improve contrastive learning by sampling mini-batches from the input data. |
Zhen Yang; Tinglin Huang; Ming Ding; Yuxiao Dong; Rex Ying; Yukuo Cen; Yangliao Geng; Jie Tang; |
| 260 | Improving The Expressiveness of K-hop Message-Passing GNNs By Injecting Contextualized Substructure Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we discuss the limitation of K-hop message-passing GNNs and propose substructure encoding function to uplift the expressive power of any K-hop message-passing GNN. |
Tianjun Yao; Yingxu Wang; Kun Zhang; Shangsong Liang; |
| 261 | Web-based Long-term Spine Treatment Outcome Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite a few machine-learning-based methods have been proposed for TOF, their performance and feasibility are mostly unsatisfactory due to the neglect of a few practical challenges (caused by applying on the Internet), including biased data selection, noisy supervision, and patient noncompliance. In light of this, we propose DeepTOF, a novel end-to-end deep learning model to cope with the unique challenges in web-based long-term continuous spine TOF. |
Hangting Ye; Zhining Liu; Wei Cao; Amir M. Amiri; Jiang Bian; Yi Chang; Jon D. Lurie; Jim Weinstein; Tie-Yan Liu; |
| 262 | PAT: Geometry-Aware Hard-Label Black-Box Adversarial Attacks on Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite a plethora of prior explorations, conducting text adversarial attacks in practical settings is still challenging with the following constraints: black box — the inner structure of the victim model is unknown; hard label — the attacker only has access to the top-1 prediction results; and semantic preservation – the perturbation needs to preserve the original semantics. In this paper, we present PAT, a novel adversarial attack method employed under all these constraints. |
Muchao Ye; Jinghui Chen; Chenglin Miao; Han Liu; Ting Wang; Fenglong Ma; |
| 263 | VQNE: Variational Quantum Network Embedding with Application to Network Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It differs from the general setting of graph node embedding whereby the node attributes are also considered and yet may incur privacy issues. In this paper, we depart from the classic CPU/GPU architecture to consider the well-established network alignment problem based on network embedding, and develop a quantum machine learning approach with a low qubit cost for its near-future applicability on Noisy Intermediate-Scale Quantum (NISQ) devices. |
Xinyu Ye; Ge Yan; Junchi Yan; |
| 264 | Optimal Dynamic Subset Sampling: Theory and Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the fundamental problem of sampling independent events, called subset sampling. |
Lu Yi; Hanzhi Wang; Zhewei Wei; |
| 265 | Less Is More: SlimG for Accurate, Robust, and Interpretable Graph Mining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on the principle, we propose SlimG for semi-supervised node classification, which exhibits four desirable properties: It is (a) accurate, winning or tying on 10 out of 13 real-world datasets; (b) robust, being the only one that handles all scenarios of graph data (homophily, heterophily, random structure, noisy features, etc.); (c) fast and scalable, showing up to 18 times faster training in million-scale graphs; and (d) interpretable, thanks to the linearity and sparsity. |
Jaemin Yoo; Meng-Chieh Lee; Shubhranshu Shekhar; Christos Faloutsos; |
| 266 | FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce FLAMES2Graph, a new horizontal federated learning framework designed to interpret the deep learning decisions of each client. |
Raneen Younis; Zahra Ahmadi; Abdul Hakmeh; Marco Fisichella; |
| 267 | Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, in this paper, we aim to adaptively evolve the model to select proper operations to interact on feature pairs under task guidance. |
Runlong Yu; Xiang Xu; Yuyang Ye; Qi Liu; Enhong Chen; |
| 268 | Towards Variance Reduction for Reinforcement Learning of Industrial Decision-making Tasks: A Bi-Critic Based Demand-Constraint Decoupling Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing efforts design novel value functions to alleviate the issue but still suffer. In this paper, we address this issue from the perspective of adjusting the actor-critic paradigm. |
Jianyong Yuan; Jiayi Zhang; Zinuo Cai; Junchi Yan; |
| 269 | Spatio-temporal Diffusion Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel parameterization framework for STPPs, which leverages diffusion models to learn complex spatio-temporal joint distributions. |
Yuan Yuan; Jingtao Ding; Chenyang Shao; Depeng Jin; Yong Li; |
| 270 | Sharpness-Aware Minimization Revisited: Weighted Sharpness As A Regularization Term Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. |
Yun Yue; Jiadi Jiang; Zhiling Ye; Ning Gao; Yongchao Liu; Ke Zhang; |
| 271 | Doubly Robust AUC Optimization Against Noisy and Adversarial Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the statistical upper bound, we propose our optimization objective followed by an efficient alternatively stochastic descent algorithm, which can effectively improve the performance of learning models by guarding against adversarial samples and noisy samples. |
Chenkang Zhang; Wanli Shi; Lei Luo; Bin Gu; |
| 272 | Hyperbolic Graph Topic Modeling Network with Continuously Updated Topic Tree Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a Hyperbolic Graph Topic Modeling Network to integrate both network hierarchy across linked documents and semantic hierarchy within texts into a unified HGNN framework. |
Delvin Ce Zhang; Rex Ying; Hady W. Lauw; |
| 273 | Quantifying Node Importance Over Network Structural Stability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing studies validate that the coreness of a node is the "best practice" on network topology to estimate the engagement of the node. In this paper, the importance of a node is the effect on the engagement of other nodes when its engagement is strengthened or weakened. |
Fan Zhang; Qingyuan Linghu; Jiadong Xie; Kai Wang; Xuemin Lin; Wenjie Zhang; |
| 274 | Finding Favourite Tuples on Data Streams with Provably Few Comparisons Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the problem of identifying one or more high-utility tuples by adaptively receiving user input on a minimum number of pairwise comparisons. |
Guangyi Zhang; Nikolaj Tatti; Aristides Gionis; |
| 275 | HiMacMic: Hierarchical Multi-Agent Deep Reinforcement Learning with Dynamic Asynchronous Macro Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a hierarchical MADRL framework called "HiMacMic" with dynamic asynchronous macro strategy. |
Hancheng Zhang; Guozheng Li; Chi Harold Liu; Guoren Wang; Jian Tang; |
| 276 | FedCP: Separating Feature Information for Personalized Federated Learning Via Conditional Policy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. |
Jianqing Zhang; Yang Hua; Hao Wang; Tao Song; Zhengui Xue; Ruhui Ma; Haibing Guan; |
| 277 | A Study of Situational Reasoning for Traffic Understanding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Whereas prior work has provided benchmarks and methods for traffic monitoring, it remains unclear whether models can effectively align these information sources and reason in novel scenarios. To address this assessment gap, we devise three novel text-based tasks for situational reasoning in the traffic domain: i) BDD-QA, which evaluates the ability of Language Models (LMs) to perform situational decision-making, ii) TV-QA, which assesses LMs’ abilities to reason about complex event causality, and iii) HDT-QA, which evaluates the ability of models to solve human driving exams. |
Jiarui Zhang; Filip Ilievski; Kaixin Ma; Aravinda Kollaa; Jonathan Francis; Alessandro Oltramari; |
| 278 | Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, recent advances in irregular time series have primarily focused on addressing intra-series irregularity, overlooking the issue of inter-series discrepancy. To bridge this gap, we present Warpformer, a novel approach that fully considers these two characteristics. |
Jiawen Zhang; Shun Zheng; Wei Cao; Jiang Bian; Jia Li; |
| 279 | MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Driven by the generalized GIB, we propose a graph mixup method, MixupExplainer, with a theoretical guarantee to resolve the distribution shifting issue. |
Jiaxing Zhang; Dongsheng Luo; Hua Wei; |
| 280 | Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set. In this paper, we focus on a more general yet practical setting, non-identical client class sets, where clients focus on their own (different or even non-overlapping) class sets and seek a global model that works for the union of these classes. |
Jiayun Zhang; Xiyuan Zhang; Xinyang Zhang; Dezhi Hong; Rajesh K. Gupta; Jingbo Shang; |
| 281 | DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively. To further enhance the disentanglement of these two types of representation, we propose a disentanglement-aware discriminator under an adversarial learning framework from the perspective of information theory. |
Kaike Zhang; Qi Cao; Gaolin Fang; Bingbing Xu; Hongjian Zou; Huawei Shen; Xueqi Cheng; |
| 282 | Rumor Detection with Diverse Counterfactual Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, these methods suffer from less robust results as they employ all the propagation patterns for rumor detection. In this paper, we address the above issues with the proposed Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). |
Kaiwei Zhang; Junchi Yu; Haichao Shi; Jian Liang; Xiao-Yu Zhang; |
| 283 | CFGL-LCR: A Counterfactual Graph Learning Framework for Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a counterfactual graph learning framework for legal case retrieval. |
Kun Zhang; Chong Chen; Yuanzhuo Wang; Qi Tian; Long Bai; |
| 284 | Efficient Single-Source SimRank Query By Path Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose VecSim for efficient single-source SimRank query by path aggregation. |
Mingxi Zhang; Yanghua Xiao; Wei Wang; |
| 285 | Debiasing Recommendation By Learning Identifiable Latent Confounders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. |
Qing Zhang; Xiaoying Zhang; Yang Liu; Hongning Wang; Min Gao; Jiheng Zhang; Ruocheng Guo; |
| 286 | Local Boosting for Weakly-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that the standard implementation of the convex combination of base learners can hardly work due to the presence of noisy labels. |
Rongzhi Zhang; Yue Yu; Jiaming Shen; Xiquan Cui; Chao Zhang; |
| 287 | Capacity Constrained Influence Maximization in Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. |
Shiqi Zhang; Yiqian Huang; Jiachen Sun; Wenqing Lin; Xiaokui Xiao; Bo Tang; |
| 288 | Efficient Approximation Algorithms for Spanning Centrality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing techniques fail to deal with such graphs, as they either suffer from expensive matrix operations or require sampling numerous long random walks. To circumvent these issues, this paper proposes TGT and its enhanced version TGT+, two algorithms for AESC computation that offers rigorous theoretical approximation guarantees. |
Shiqi Zhang; Renchi Yang; Jing Tang; Xiaokui Xiao; Bo Tang; |
| 289 | DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present DM-PFL, a novel framework that utilizes a dual masking mechanism to train both global and personalized models with weight-level parameter sharing and end-to-end sparse training. |
Wenhao Zhang; Zimu Zhou; Yansheng Wang; Yongxin Tong; |
| 290 | Domain-Specific Risk Minimization for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first establish a generalization bound that explicitly considers the adaptivity gap. Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. |
Yi-Fan Zhang; Jindong Wang; Jian Liang; Zhang Zhang; Baosheng Yu; Liang Wang; Dacheng Tao; Xing Xie; |
| 291 | Contrastive Cross-scale Graph Knowledge Synergy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Efforts are mainly focused on gathering more global information via contrasting on a single high-level graph view, which, however, underestimates the inherent complex and hierarchical properties in many real-world networks, leading to sub-optimal embeddings. To incorporate these properties of a complex graph, we propose Cross-Scale Contrastive Graph Knowledge Synergy (CGKS), a generic feature learning framework, to advance graph contrastive learning with enhanced generalization ability and the awareness of latent anatomies. |
Yifei Zhang; Yankai Chen; Zixing Song; Irwin King; |
| 292 | Adaptive Disentangled Transformer for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of layer-wise disentanglement for Transformer architectures and propose the Adaptive Disentangled Transformer (ADT) framework, which is able to adaptively determine the optimal degree of disentanglement of attention heads within different layers. |
Yipeng Zhang; Xin Wang; Hong Chen; Wenwu Zhu; |
| 293 | AdaProp: Learning Adaptive Propagation for Graph Neural Network Based Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we are motivated to learn an adaptive propagation path in order to filter out irrelevant entities while preserving promising targets. |
Yongqi Zhang; Zhanke Zhou; Quanming Yao; Xiaowen Chu; Bo Han; |
| 294 | Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, instead of viewing the entire paper as a long linear sequence, one should exploit the structural information such as citation links across papers and the hierarchy of sections and paragraphs in each paper. To tackle these challenges, in this study, we propose FUTEX, a framework that uses the cross-paper network structure and the in-paper hierarchy structure to classify full-text scientific papers under weak supervision. |
Yu Zhang; Bowen Jin; Xiusi Chen; Yanzhen Shen; Yunyi Zhang; Yu Meng; Jiawei Han; |
| 295 | Hierarchical Invariant Learning for Domain Generalization Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we figure out domain generalization recommendation with a clear symbolized definition and propose corresponding models. |
Zeyu Zhang; Heyang Gao; Hao Yang; Xu Chen; |
| 296 | Towards Fair Disentangled Online Learning for Changing Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor. |
Chen Zhao; Feng Mi; Xintao Wu; Kai Jiang; Latifur Khan; Christan Grant; Feng Chen; |
| 297 | DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. |
Lifan Zhao; Shuming Kong; Yanyan Shen; |
| 298 | Spatial Clustering Regression of Count Value Data Via Bayesian Mixture of Finite Mixtures Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on a Bayesian mixture of finite mixtures model, we present a novel spatially clustered coefficients regression model for count value data. |
Peng Zhao; Hou-Cheng Yang; Dipak K. Dey; Guanyu Hu; |
| 299 | Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Some pioneering works have been proposed, but they suffer from many limitations, e.g., assuming a demonstration to be of the same optimality throughout time steps and failing to provide any interpretation w.r.t knowledge learned from the noisy set. Addressing these problems, we propose \method by evaluating and imitating at the sub-demonstration level, encoding action primitives of varying quality into different skills. |
Tianxiang Zhao; Wenchao Yu; Suhang Wang; Lu Wang; Xiang Zhang; Yuncong Chen; Yanchi Liu; Wei Cheng; Haifeng Chen; |
| 300 | GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate these issues, this paper explores a new direction that moves forward to learn a universal structure learning model that can generalize across graph datasets in an open world. |
Wentao Zhao; Qitian Wu; Chenxiao Yang; Junchi Yan; |
| 301 | Generative Causal Interpretation Model for Spatio-Temporal Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we regard the causal mechanism as a spatio-temporal causal process modulated by non-stationary exogenous variables. |
Yu Zhao; Pan Deng; Junting Liu; Xiaofeng Jia; Jianwei Zhang; |
| 302 | Improving Search Clarification with Structured Information Extracted from Search Results Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we emphasize the importance of structured information in search results for improving search clarification. |
Ziliang Zhao; Zhicheng Dou; Yu Guo; Zhao Cao; Xiaohua Cheng; |
| 303 | Dense Representation Learning and Retrieval for Tabular Data Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, some retrieval-based methods for tabular data label prediction have been proposed, which, however, treat the data as sparse vectors to perform the retrieval, which fails to make use of the semantic information of the tabular data. To address such a problem, in this paper, we propose a novel framework of dense retrieval on tabular data (DERT) to support flexible data representation learning and effective label prediction on tabular data. |
Lei Zheng; Ning Li; Xianyu Chen; Quan Gan; Weinan Zhang; |
| 304 | Automatic Temporal Relation in Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we propose a novel mechanism named Automatic Temporal Relation (AutoTR) for directly and automatically learning the temporal relation from any given dataset. |
Menghui Zhou; Po Yang; |
| 305 | Narrow The Input Mismatch in Deep Graph Neural Network Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We call this gap "input mismatch". To alleviate this problem, we propose a lightweight stochastic extended module to provide an estimation for missing input information for student GNNs. |
Qiqi Zhou; Yanyan Shen; Lei Chen; |
| 306 | A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks Via Edge Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the opinion maximization problem for the leader-follower DeGroot model of opinion dynamics in a social network modelled by a directed graph with n nodes, where a small number of nodes are competing leader nodes with binary opposing opinions 0 or 1, and the rest are follower nodes. |
Xiaotian Zhou; Liwang Zhu; Wei Li; Zhongzhi Zhang; |
| 307 | Maintaining The Status Quo: Capturing Invariant Relations for OOD Spatiotemporal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we find that ST relations make sense for generalization and devise a Causal ST learning framework, CauSTG, which enables invariant relation transferred to OOD scenarios. |
Zhengyang Zhou; Qihe Huang; Kuo Yang; Kun Wang; Xu Wang; Yudong Zhang; Yuxuan Liang; Yang Wang; |
| 308 | Dual-view Molecular Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to leverage both representations and design a new pre-training algorithm, dual-view molecule pre-training (briefly, DVMP), that can effectively combine the strengths of both types of molecule representations. |
Jinhua Zhu; Yingce Xia; Lijun Wu; Shufang Xie; Wengang Zhou; Tao Qin; Houqiang Li; Tie-Yan Liu; |
| 309 | On Structural Expressive Power of Graph Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the connections between Weisfeiler-Lehman (WL) graph isomorphism test and graph neural network (GNN), we introduce SEG-WL test (Structural Encoding enhanced G lobal Weisfeiler-Lehman test), a generalized graph isomorphism test algorithm as a powerful theoretical tool for exploring the structural discriminative power of graph Transformers. |
Wenhao Zhu; Tianyu Wen; Guojie Song; Liang Wang; Bo Zheng; |
| 310 | Path-Specific Counterfactual Fairness for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: But since sensitive features may also affect user interests in a fair manner (e.g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities. To address this challenge, we propose a path-specific fair RS (PSF-RS) for recommendations. |
Yaochen Zhu; Jing Ma; Liang Wu; Qi Guo; Liangjie Hong; Jundong Li; |
| 311 | WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, previous dynamic GNN models are under the same fixed temporal term, which causes the short-temporal optimum. To address these issues, we propose the WinGNN framework to model dynamic graphs, which is realized by a simple GNN model with the meta-learning strategy and a novel mechanism of random gradient aggregation. |
Yifan Zhu; Fangpeng Cong; Dan Zhang; Wenwen Gong; Qika Lin; Wenzheng Feng; Yuxiao Dong; Jie Tang; |
| 312 | Robust Positive-Unlabeled Learning Via Noise Negative Sample Self-correction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first. |
Zhangchi Zhu; Lu Wang; Pu Zhao; Chao Du; Wei Zhang; Hang Dong; Bo Qiao; Qingwei Lin; Saravan Rajmohan; Dongmei Zhang; |
| 313 | DyGen: Learning from Noisy Labels Via Dynamics-Enhanced Generative Modeling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose the Dynamics-Enhanced Generative Model (DyGen), which uses dynamic patterns in the embedding space during the fine-tuning process of language models to improve noisy label predictions. |
Yuchen Zhuang; Yue Yu; Lingkai Kong; Xiang Chen; Chao Zhang; |
| 314 | CADENCE: Offline Category Constrained and Diverse Query Generation for E-commerce Autosuggest Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Much of the recent works generate synthetic candidates using models trained on user queries and thus have these issues: a) cold start problem as new products in the catalogue fail to get visibility due to lack of representation in user queries b) poor quality of generated candidates due to concept drift and c) low diversity/coverage of attributes such as brand, color & other facets in generated candidates. In this paper, we propose an offline neural query generation framework – CADENCE – to address these challenges by a) using both user queries and noisy product titles to train two separate neural language models using self-attention memory networks, b) adding category constraints during the training and query generation process to prevent concept drift c) implementing customized dynamic beam search to generate more diverse candidates for a given prefix. |
Abhinav Anand; Surender Kumar; Nandeesh Kumar; Samir Shah; |
| 315 | Learning to Solve Grouped 2D Bin Packing Problems in The Manufacturing Industry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, factories manufacture hundreds of customer orders (sets of items) every day, and to relieve pressure in management, a common practice is to group the orders into batches for production, ensuring that items from one order are in the same batch instead of scattered across the production line. In this work, we formulate this problem as the grouped 2D bin packing problem, a bi-level problem where the upper level partitions orders into groups and the lower level solves 2DBP for items in each group. |
Wenxuan Ao; Guozhen Zhang; Yong Li; Depeng Jin; |
| 316 | Fusing Multimodal Signals on Hyper-complex Space for Extreme Abstractive Text Summarization (TL;DR) of Scientific Contents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we deal with a novel task of extreme abstractive text summarization (aka TL;DR generation) by leveraging multiple input modalities. |
Yash Kumar Atri; Vikram Goyal; Tanmoy Chakraborty; |
| 317 | SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the ability of transformer-based language models (LMs) to understand social media language. |
Vasilisa Bashlovkina; Riley Matthews; Zhaobin Kuang; Simon Baumgartner; Michael Bendersky; |
| 318 | Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. |
Jacob Brown; Xi Jiang; Van Tran; Arjun Nitin Bhagoji; Nguyen Phong Hoang; Nick Feamster; Prateek Mittal; Vinod Yegneswaran; |
| 319 | RankFormer: Listwise Learning-to-Rank Using Listwide Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose the RankFormer as an architecture that, with a Transformer at its core, can jointly optimize a novel listwide assessment objective and a traditional listwise LTR objective. |
Maarten Buyl; Paul Missault; Pierre-Antoine Sondag; |
| 320 | Capturing Conversion Rate Fluctuation During Sales Promotions: A Novel Historical Data Reuse Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Herein, we propose a novel Historical Data Reuse (HDR) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. |
Zhangming Chan; Yu Zhang; Shuguang Han; Yong Bai; Xiang-Rong Sheng; Siyuan Lou; Jiacen Hu; Baolin Liu; Yuning Jiang; Jian Xu; Bo Zheng; |
| 321 | TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose TWo-stage Interest Network (TWIN), where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. |
Jianxin Chang; Chenbin Zhang; Zhiyi Fu; Xiaoxue Zang; Lin Guan; Jing Lu; Yiqun Hui; Dewei Leng; Yanan Niu; Yang Song; Kun Gai; |
| 322 | PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a plug-and-play Parameter and Embedding Personalized Network (PEPNet) for multi-domain and multi-task recommendation. |
Jianxin Chang; Chenbin Zhang; Yiqun Hui; Dewei Leng; Yanan Niu; Yang Song; Kun Gai; |
| 323 | Taming The Domain Shift in Multi-source Learning for Energy Disaggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We first formulate an unsupervised multi-source domain adaptation problem to address this challenge by leveraging rich public datasets for building the NILM model. Then, we prove a new generalization bound for the target domain under multi-source settings. |
Xiaomin Chang; Wei Li; Yunchuan Shi; Albert Y. Zomaya; |
| 324 | Web-Scale Academic Name Disambiguation: The WhoIsWho Benchmark, Leaderboard, and Toolkit Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present Who Is Who owning, a large-scale benchmark with over 1,000,000 papers built using an interactive annotation process, a regular leaderboard with comprehensive tasks, and an easy-to-use toolkit encapsulating the entire pipeline as well as the most powerful features and baseline models for tackling the tasks. |
Bo Chen; Jing Zhang; Fanjin Zhang; Tianyi Han; Yuqing Cheng; Xiaoyan Li; Yuxiao Dong; Jie Tang; |
| 325 | FS-REAL: Towards Real-World Cross-Device Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, in this paper, we propose an efficient and scalable prototyping system for real-world cross-device FL, FS-REAL. |
Daoyuan Chen; Dawei Gao; Yuexiang Xie; Xuchen Pan; Zitao Li; Yaliang Li; Bolin Ding; Jingren Zhou; |
| 326 | A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e.g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem. |
Liyue Chen; Jiangyi Fang; Zhe Yu; Yongxin Tong; Shaosheng Cao; Leye Wang; |
| 327 | Controllable Multi-Objective Re-ranking with Policy Hypernetworks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights. |
Sirui Chen; Yuan Wang; Zijing Wen; Zhiyu Li; Changshuo Zhang; Xiao Zhang; Quan Lin; Cheng Zhu; Jun Xu; |
| 328 | Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. |
Xiaohui Chen; Jiankai Sun; Taiqing Wang; Ruocheng Guo; Li-Ping Liu; Aonan Zhang; |
| 329 | Binary Classifier Evaluation on Unlabeled Segments Using Inverse Distance Weighting with Distance Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a novel methodology to estimate the performance of a binary classifier in segments of the population where labels are unavailable. |
Xu Chen; Katerina Marazopoulou; Wesley Lee; Christine Agarwal; Jason Sukumaran; Aude Hofleitner; |
| 330 | Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. |
Ling Cheng; Feida Zhu; Yong Wang; Ruicheng Liang; Huiwen Liu; |
| 331 | CT4Rec: Simple Yet Effective Consistency Training for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. |
Liu Chong; Xiaoyang Liu; Rongqin Zheng; Lixin Zhang; Xiaobo Liang; Juntao Li; Lijun Wu; Min Zhang; Leyu Lin; |
| 332 | Conditional Neural ODE Processes for Individual Disease Progression Forecasting: A Case Study on COVID-19 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the promises in the existing ML techniques, a variety of unique challenges emerge for disease progression forecasting, such as irregularly-sampled time series, data sparsity, and individual heterogeneity in disease progression. To tackle these challenges, we propose novel Conditional Neural Ordinary Differential Equations Processes (CNDPs), and validate it in a COVID-19 disease progression forecasting task using audio data. |
Ting Dang; Jing Han; Tong Xia; Erika Bondareva; Chloë Siegele-Brown; Jagmohan Chauhan; Andreas Grammenos; Dimitris Spathis; Pietro Cicuta; Cecilia Mascolo; |
| 333 | Modelling Delayed Redemption with Importance Sampling and Pre-Redemption Engagement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Estimating the likelihood of a customer redeeming an incentive is challenging due to 1) data sparsity: relatively rare occurrence of coupon redemptions as compared to issuances, and 2) delayed feedback: customers taking time to redeem, resulting in inaccurate model refresh, compounded by data drift due to new customers and coupons. To overcome these challenges, we present a novel framework, DRESS (Delayed Redemption Entire Space Sampling), that jointly models the effect of data sparsity and delayed feedback on redemptions. |
Samik Datta; Anshuman Mourya; Anirban Majumder; Vineet Chaoji; |
| 334 | Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present two novel methods for variance reduction that rely exclusively on in-experiment data. |
Alex Deng; Michelle Du; Anna Matlin; Qing Zhang; |
| 335 | From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop and compare methods that solve final exams, which differ from problem sets in several ways: the questions are longer, have multiple parts, are more complicated, and span a broader set of topics. |
Iddo Drori; Sarah J. Zhang; Reece Shuttleworth; Sarah Zhang; Keith Tyser; Zad Chin; Pedro Lantigua; Saisamrit Surbehera; Gregory Hunter; Derek Austin; Leonard Tang; Yann Hicke; Sage Simhon; Sathwik Karnik; Darnell Granberry; Madeleine Udell; |
| 336 | Time-to-Event Modeling with Hypernetwork Based Hawkes Process Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite having massive amounts of event sequence data from various domains like social media, healthcare etc., real world application of temporal point process faces two major challenges: 1) it is not generalizable to predict events from unseen event sequences in dynamic environment 2) they are not capable of thriving in continually evolving environment with minimal supervision while retaining previously learnt knowledge. To tackle these issues, we propose HyperHawkes, a hypernetwork based temporal point process framework which is capable of modeling time of event occurrence for unseen sequences and consequently, zero-shot learning for time-to-event modeling. |
Manisha Dubey; P.K. Srijith; Maunendra Sankar Desarkar; |
| 337 | Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. |
Mohannad Elhamod; Mridul Khurana; Harish Babu Manogaran; Josef C. Uyeda; Meghan A. Balk; Wasila Dahdul; Yasin Bakis; Henry L. Bart; Paula M. Mabee; Hilmar Lapp; James P. Balhoff; Caleb Charpentier; David Carlyn; Wei-Lun Chao; Charles V. Stewart; Daniel I. Rubenstein; Tanya Berger-Wolf; Anuj Karpatne; |
| 338 | Demystifying Fraudulent Transactions and Illicit Nodes in The Bitcoin Network for Financial Forensics Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a holistic applied data science approach to fraud detection in the Bitcoin network with two original contributions. First, we contribute the Elliptic++dataset, which extends the Elliptic transaction dataset to include over 822k Bitcoin wallet addresses (nodes), each with 56 features, and 1.27M temporal interactions. |
Youssef Elmougy; Ling Liu; |
| 339 | RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a novel skill-aware prompt-based pretraining framework, namely RecruitPro, which is capable of learning unified representations on the recruitment data and adapting for various downstream tasks of intelligent recruitment services. |
Chuyu Fang; Chuan Qin; Qi Zhang; Kaichun Yao; Jingshuai Zhang; Hengshu Zhu; Fuzhen Zhuang; Hui Xiong; |
| 340 | Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. Specifically, we treat every agent as an individual operator to trade one specific order, while keeping communicating with each other and collaborating for maximizing the overall profits. |
Yuchen Fang; Zhenggang Tang; Kan Ren; Weiqing Liu; Li Zhao; Jiang Bian; Dongsheng Li; Weinan Zhang; Yong Yu; Tie-Yan Liu; |
| 341 | A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, this paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification, which relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability). |
Kevin Fauvel; Fuxing Chen; Dario Rossi; |
| 342 | ILRoute: A Graph-based Imitation Learning Method to Unveil Riders’ Routing Strategies in Food Delivery Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there still exist three main challenges: (1) the rider’s route decision is affected by multi-source and heterogeneous features and the complex relationships among these features make it hard to explore how they influence the rider’s route decision-making; (2) the large route decision-making space make it easy to explore and predict unreasonable routes; (3) the rider’s personalized preference is important in modeling the route decision-making process but cannot be fully explored. To tackle the above challenges, we propose ILRoute, a Graph-based imitation learning method for PDRP. |
Tao Feng; Huan Yan; Huandong Wang; Wenzhen Huang; Yuyang Han; Hongsen Liao; Jinghua Hao; Yong Li; |
| 343 | FedMultimodal: A Benchmark for Multimodal Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities. |
Tiantian Feng; Digbalay Bose; Tuo Zhang; Rajat Hebbar; Anil Ramakrishna; Rahul Gupta; Mi Zhang; Salman Avestimehr; Shrikanth Narayanan; |
| 344 | Influence Maximization with Fairness at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes (e.g., gender, location, origin, race, etc.) are fairly affected, i.e., are proportionally similar between the original network and the affected users. |
Yuting Feng; Ankitkumar Patel; Bogdan Cautis; Hossein Vahabi; |
| 345 | Binary Embedding-based Retrieval at Tencent Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further realize efficient search, we propose Symmetric Distance Calculation (SDC) to achieve lower response time than Hamming codes. |
Yukang Gan; Yixiao Ge; Chang Zhou; Shupeng Su; Zhouchuan Xu; Xuyuan Xu; Quanchao Hui; Xiang Chen; Yexin Wang; Ying Shan; |
| 346 | Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The library has been developed organically since 2018 following a set of four design principles applicable to machine learning libraries and frameworks: simplicity of use, safety of use, modularity and high-level abstraction, and integration with other machine learning libraries. In this paper, we describe those principles in detail and present how they have been used to guide the design of the library. |
Mathieu Guillame-Bert; Sebastian Bruch; Richard Stotz; Jan Pfeifer; |
| 347 | Towards Equitable Assignment: Data-Driven Delivery Zone Partition at Last-mile Logistics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To generate delivery zone partition with equitable workload assignment, in this paper, we propose E-partition, a data-driven delivery zone partition framework to achieve equitable workload assignment in last-mile logistics. |
Baoshen Guo; Shuai Wang; Haotian Wang; Yunhuai Liu; Fanshuo Kong; Desheng Zhang; Tian He; |
| 348 | An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, most recent models are practically impossible to interpret or musically fine-tune, as they use deep neural networks with thousands of parameters. We introduce an interpretable, flexible, and interactive model, SchenkComposer, for melody generation that empowers users to be creative in all aspects of the music generation pipeline and allows them to learn from the process. |
Stephen Hahn; Rico Zhu; Simon Mak; Cynthia Rudin; Yue Jiang; |
| 349 | IETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a robust and scalable incremental ETA learning framework, iETA, to continuously exploit spatio-temporal traffic patterns from massive floating-car data and thus achieve better estimation performances. |
Jindong Han; Hao Liu; Shui Liu; Xi Chen; Naiqiang Tan; Hua Chai; Hui Xiong; |
| 350 | Efficient Continuous Space Policy Optimization for High-frequency Trading Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing DRL-based methods either leverage portfolio optimization on low-frequency scenarios or only support a very limited number of assets with discrete action space, facing significant computing efficiency challenges. Therefore, we propose an efficient DRL-based policy optimization (DRPO) method for high-frequency trading. |
Li Han; Nan Ding; Guoxuan Wang; Dawei Cheng; Yuqi Liang; |
| 351 | Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. |
Weiguang Han; Boyi Zhang; Qianqian Xie; Min Peng; Yanzhao Lai; Jimin Huang; |
| 352 | Identifying Complicated Contagion Scenarios from Cascade Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the question of whether the structural properties of a (partially) observed cascade can characterize the contagion scenario and identify the interventions that might be in effect. |
Galen Harrison; Amro Alabsi Aljundi; Jiangzhuo Chen; S.S. Ravi; Anil Kumar Vullikanti; Madhav V. Marathe; Abhijin Adiga; |
| 353 | BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a Bilateral Occupational-Suitability-aware recommender System (BOSS) for online recruitment, in consideration of the reciprocal, bilateral, and sequential properties of realistic recruitment scenarios simultaneously. |
Xiao Hu; Yuan Cheng; Zhi Zheng; Yue Wang; Xinxin Chi; Hengshu Zhu; |
| 354 | Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Different Visual Question Answering (VQA) task. |
Xinyue Hu; Lin Gu; Qiyuan An; Mengliang Zhang; Liangchen Liu; Kazuma Kobayashi; Tatsuya Harada; Ronald M. Summers; Yingying Zhu; |
| 355 | Graph Learning in Physical-informed Mesh-reduced Space for Real-world Dynamic Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these learning-based models suffer from scalability issues when training on high-dimensional and high-resolution simulation data generated for real-world applications. In this work, we aim to tackle this challenge by deliberately prioritizing certain aspects of dynamic systems, while allocating relatively less attention and computational resources to others. |
Yeping Hu; Bo Lei; Victor M. Castillo; |
| 356 | SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that the heterogeneity in multi-scenario recommendations is a key problem that needs to be solved. |
Zhaoxin Huan; Ang Li; Xiaolu Zhang; Xu Min; Jieyu Yang; Yong He; Jun Zhou; |
| 357 | Learning Discrete Document Representations in Web Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Two-stage Multi-task Joint training technique (TMJ) to learn discrete document representations, which is simple and effective for real-world practical applications. |
Rong Huang; Danfeng Zhang; Weixue Lu; Han Li; Meng Wang; Daiting Shi; Jun Fan; Zhicong Cheng; Simiu Gu; Dawei Yin; |
| 358 | Large-scale Urban Cellular Traffic Generation Via Knowledge-Enhanced GANs with Multi-Periodic Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a knowledge-enhanced GAN with multi-periodic patterns to generate large-scale cellular traffic based on the urban environment. |
Shuodi Hui; Huandong Wang; Tong Li; Xinghao Yang; Xing Wang; Junlan Feng; Lin Zhu; Chao Deng; Pan Hui; Depeng Jin; Yong Li; |
| 359 | SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and Its Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a Bangla multi-domain sentiment analysis dataset, named as SentiGOLD, developed using 70,000 samples, which was compiled from a variety of sources and annotated by a gender-balanced team of linguists. |
Md. Ekramul Islam; Labib Chowdhury; Faisal Ahamed Khan; Shazzad Hossain; Md Sourave Hossain; Mohammad Mamun Or Rashid; Nabeel Mohammed; Mohammad Ruhul Amin; |
| 360 | Off-Policy Learning-to-Bid with AuctionGym Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work provides a unified framework for such learning to bid” methods, showing how many existing approaches fall under the value-based paradigm. |
Olivier Jeunen; Sean Murphy; Ben Allison; |
| 361 | FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the problem, in this paper, we design a Fairness-aware Concurrent dispatch system called FairCod, which aims to optimize the overall operation efficiency and individual fairness at the same time. |
Lin Jiang; Shuai Wang; Baoshen Guo; Hai Wang; Desheng Zhang; Guang Wang; |
| 362 | AdSEE: Investigating The Impact of Image Style Editing on Advertisement Attractiveness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Advertisement Style Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic editing to ads images can affect or alter the popularity of online advertisements. |
Liyao Jiang; Chenglin Li; Haolan Chen; Xiaodong Gao; Xinwang Zhong; Yang Qiu; Shani Ye; Di Niu; |
| 363 | Adaptive Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. |
Yangqin Jiang; Chao Huang; Lianghao Huang; |
| 364 | PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose PGLBox1 – a multi-GPU graph learning framework based on PaddlePaddle [24], incorporating with optimized storage, computation, and communication strategies, to train deep GNNs based on web-scale graphs for the recommendation. |
Xuewu Jiao; Weibin Li; Xinxuan Wu; Wei Hu; Miao Li; Jiang Bian; Siming Dai; Xinsheng Luo; Mingqing Hu; Zhengjie Huang; Danlei Feng; Junchao Yang; Shikun Feng; Haoyi Xiong; Dianhai Yu; Shuanglong Li; Jingzhou He; Yanjun Ma; Lin Liu; |
| 365 | Multimodal Indoor Localisation in Parkinson’s Disease for Detecting Medication Use: Observational Pilot Study in A Free-Living Setting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the effectiveness of current indoor localisation methods, a transformer-based approach utilising dual modalities which provide complementary views of movement, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, is proposed. |
Ferdian Jovan; Catherine Morgan; Ryan McConville; Emma L. Tonkin; Ian Craddock; Alan Whone; |
| 366 | IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce the Illinois Graph Benchmark (IGB), a research dataset tool that the developers can use to train, scrutinize and systematically evaluate GNN models with high fidelity. |
Arpandeep Khatua; Vikram Sharma Mailthody; Bhagyashree Taleka; Tengfei Ma; Xiang Song; Wen-mei Hwu; |
| 367 | Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. |
Hyunsung Kim; Han-Jun Choi; Chang Jo Kim; Jinsung Yoon; Sang-Ki Ko; |
| 368 | Real Time Index and Search Across Large Quantities of GNN Experts for Low Latency Online Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional spatio-temporal and incremental MoE frameworks struggle with poor inference accuracy and linear time complexity when scaling experts, for the latter, leading to prohibitively high latency in model updates. To address this issue, we introduce the Indexed Router, a novel method that categorizes experts into a structured hierarchy called the indexed tree. |
Johan Kok Zhi Kang; Sien Yi Tan; Bingsheng He; Zhen Zhang; |
| 369 | Neural Insights for Digital Marketing Content Design Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a neural-network-based system that scores and extracts insights from a marketing content design. |
Fanjie Kong; Yuan Li; Houssam Nassif; Tanner Fiez; Ricardo Henao; Shreya Chakrabarti; |
| 370 | Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we present GOTHate, a large-scale code-mixed crowdsourced dataset of around 51k posts for hate speech detection from Twitter. |
Atharva Kulkarni; Sarah Masud; Vikram Goyal; Tanmoy Chakraborty; |
| 371 | A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, this paper proposes a preference-aware meta-optimization framework Meta-Pec for personalized vehicle energy consumption estimation. |
Siqi Lai; Weijia Zhang; Hao Liu; |
| 372 | Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. |
Daeun Lee; Sejung Son; Hyolim Jeon; Seungbae Kim; Jinyoung Han; |
| 373 | AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel model called Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. |
Danwei Li; Zhengyu Zhang; Siyang Yuan; Mingze Gao; Weilin Zhang; Chaofei Yang; Xi Liu; Jiyan Yang; |
| 374 | Learning Slow and Fast System Dynamics Via Automatic Separation of Time Scales Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a framework that effectively learns slow and fast system dynamics in an integrated manner. |
Ruikun Li; Huandong Wang; Yong Li; |
| 375 | DisasterNet: Causal Bayesian Networks with Normalizing Flows for Cascading Hazards Estimation from Satellite Imagery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. |
Xuechun Li; Paula M. Bürgi; Wei Ma; Hae Young Noh; David Jay Wald; Susu Xu; |
| 376 | Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on our practice at WeBank, there are three major challenges: Firstly, supervised learning is infeasible facing the extraordinarily large-scale but imbalanced data, with hundreds of millions of active accounts but only thousands of anomalies. Secondly, the real-world transactions form a sparse network with millions of isolated user groups, which overflows the expressive ability of current node-level GNNs. Thirdly, the explanation for each suspicious account is mandatory by the government for double check, which conflicts with the black-box nature of most DL models. Therefore, we proposed Diga, the first work to apply the diffusion probabilistic model to a graph anomaly detection problem with three novel techniques: the biased K-hop PageRank, the semi-supervised guided diffusion and the novel weight-sharing GNN layer. |
Xujia Li; Yuan Li; Xueying Mo; Hebing Xiao; Yanyan Shen; Lei Chen; |
| 377 | HardSATGEN: Understanding The Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On top of the observations that industrial formulae exhibit clear community structure and oversplit substructures lead to the difficulty in semantic formation of logical structures, we propose HardSATGEN, which introduces a fine-grained control mechanism to the neural split-merge paradigm for SAT formula generation to better recover the structural and computational properties of the industrial benchmarks. |
Yang Li; Xinyan Chen; Wenxuan Guo; Xijun Li; Wanqian Luo; Junhua Huang; Hui-Ling Zhen; Mingxuan Yuan; Junchi Yan; |
| 378 | Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). |
Youru Li; Zhenfeng Zhu; Xiaobo Guo; Linxun Chen; Zhouyin Wang; Yinmeng Wang; Bing Han; Yao Zhao; |
| 379 | S2phere: Semi-Supervised Pre-training for Web Search Over Heterogeneous Learning to Rank Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, inspired by the recent progress in pre-training transformers for performance advantages, we study the problem of pre-training LTR models using both labeled and unlabeled samples, especially we focus on the use of well-annotated samples in heterogeneous open-source LTR datasets to boost the performance of pre-training. |
Yuchen Li; Haoyi Xiong; Linghe Kong; Qingzhong Wang; Shuaiqiang Wang; Guihai Chen; Dawei Yin; |
| 380 | CBLab: Supporting The Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present City Brain Lab, a toolkit for scalable traffic simulation. |
Chumeng Liang; Zherui Huang; Yicheng Liu; Zhanyu Liu; Guanjie Zheng; Hanyuan Shi; Kan Wu; Yuhao Du; Fuliang Li; Zhenhui Jessie Li; |
| 381 | MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a framework for fake news detection based on MUlti- Step Evidence Retrieval enhancement (MUSER), which simulates the steps of human beings in the process of reading news, summarizing, consulting materials, and inferring whether the news is true or fake. |
Hao Liao; Jiahao Peng; Zhanyi Huang; Wei Zhang; Guanghua Li; Kai Shu; Xing Xie; |
| 382 | Analysis of COVID-19 Offensive Tweets and Their Targets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, focusing on Twitter, we present a comprehensive analysis of COVID-related offensive tweets and their targets. |
Song Liao; Ebuka Okpala; Long Cheng; Mingqi Li; Nishant Vishwamitra; Hongxin Hu; Feng Luo; Matthew Costello; |
| 383 | Balancing Approach for Causal Inference at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the limitations and improve computational efficiency, in this paper we present scalable algorithms, DistEB and DistMS, for two balancing approaches: entropy balancing [14] and MicroSynth [33]. |
Sicheng Lin; Meng Xu; Xi Zhang; Shih-Kang Chao; Ying-Kai Huang; Xiaolin Shi; |
| 384 | Tree Based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How to design a framework that solves these four issues simultaneously remain unexplored. Therefore we propose TPM (Tree-based Progressive regression Model) for watch time prediction. |
Xiao Lin; Xiaokai Chen; Linfeng Song; Jingwei Liu; Biao Li; Peng Jiang; |
| 385 | Explicit Feature Interaction-aware Uplift Network for Online Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. |
Dugang Liu; Xing Tang; Han Gao; Fuyuan Lyu; Xiuqiang He; |
| 386 | Uncertainty-Aware Probabilistic Travel Time Prediction for On-Demand Ride-Hailing at DiDi Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a probabilistic framework, ProbTTE, for uncertainty-aware travel time prediction. |
Hao Liu; Wenzhao Jiang; Shui Liu; Xi Chen; |
| 387 | Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We approach this as a recommendation problem based on three criteria: closeness (how relevant the sender and topic are to the user), timeliness (how recent the email is), and conciseness (how brief the email is). We propose MOSR (Multi-Objective Stationary Recommender), a novel online algorithm that uses an adaptive control model to dynamically balance these criteria and adapt to preference changes. |
Jiayi Liu; Jennifer Neville; |
| 388 | PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a federated news recommendation method for achieving better utility in model training and online serving under a DP guarantee. |
Ruixuan Liu; Yang Cao; Yanlin Wang; Lingjuan Lyu; Yun Chen; Hong Chen; |
| 389 | WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM). |
Xiao Liu; Hanyu Lai; Hao Yu; Yifan Xu; Aohan Zeng; Zhengxiao Du; Peng Zhang; Yuxiao Dong; Jie Tang; |
| 390 | Practical Synthetic Human Trajectories Generation Based on Variational Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, generating human trajectory data is a crucial but challenging task, which suffers from the following two critical challenges: 1) how to capture the user distribution in human trajectories (group view), and 2) how to model the complex mobility patterns of each user trajectory (individual view). In this paper, we propose a novel human trajectories generator (named VOLUNTEER), consisting of a user VAE and a trajectory VAE, to address the above challenges. |
Qingyue Long; Huandong Wang; Tong Li; Lisi Huang; Kun Wang; Qiong Wu; Guangyu Li; Yanping Liang; Li Yu; Yong Li; |
| 391 | Impact-Oriented Contextual Scholar Profiling Using Self-Citation Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a framework to compute GF over large-scale academic data sources with millions of scholars. |
Yuankai Luo; Lei Shi; Mufan Xu; Yuwen Ji; Fengli Xiao; Chunming Hu; Zhiguang Shan; |
| 392 | A Look Into Causal Effects Under Entangled Treatment in Graphs: Investigating The Impact of Contact on MRSA Infection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of causal effect estimation with treatment entangled in a graph. |
Jing Ma; Chen Chen; Anil Vullikanti; Ritwick Mishra; Gregory Madden; Daniel Borrajo; Jundong Li; |
| 393 | SAInf: Stay Area Inference of Vehicles Using Surveillance Camera Records Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design a two-stage method to solve the stay area detection problem with coarse trajectories. |
Zhipeng Ma; Chuishi Meng; Huimin Ren; Sijie Ruan; Jie Bao; Xiaoting Wang; Tianrui Li; Yu Zheng; |
| 394 | Multi-Label Learning to Rank Through Multi-Objective Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A general framework is proposed to combine label information to characterize trade-offs among goals, and allows for the use of gradient-based MOO algorithms. |
Debabrata Mahapatra; Chaosheng Dong; Yetian Chen; Michinari Momma; |
| 395 | Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we model the interdependent network as a heterogeneous graph and propose a system based on graph neural network with reinforcement learning, which can be trained on real-world data, to characterize the vulnerability of the city system accurately. |
Jinzhu Mao; Liu Cao; Chen Gao; Huandong Wang; Hangyu Fan; Depeng Jin; Yong Li; |
| 396 | DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Which considerably trims down their performance when applied in practical systems. To tackle the above issue, we present the first attempt to generalize Reinforcement Learning (RL) to the route prediction task, leading to a novel RL-based framework called DRL4Route. |
Xiaowei Mao; Haomin Wen; Hengrui Zhang; Huaiyu Wan; Lixia Wu; Jianbin Zheng; Haoyuan Hu; Youfang Lin; |
| 397 | HUGE: Huge Unsupervised Graph Embeddings with TPUs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. |
Brandon A. Mayer; Anton Tsitsulin; Hendrik Fichtenberger; Jonathan Halcrow; Bryan Perozzi; |
| 398 | Hierarchical Projection Enhanced Multi-behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, these methods are either based on the weighting of expert information extracted from the coupled input or modeling of information transfer between multiple behavior levels through task-specific extractors, which are usually accompanied by negative transfer phenomenon1. To address the above problems, we propose a multi-behavior recommendation framework, called Hierarchical Projection Enhanced Multi-behavior Recommendation (HPMR). |
Chang Meng; Hengyu Zhang; Wei Guo; Huifeng Guo; Haotian Liu; Yingxue Zhang; Hongkun Zheng; Ruiming Tang; Xiu Li; Rui Zhang; |
| 399 | Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Scenario-Adaptive Feature Interaction framework named SATrans, which models scenario discrepancy as the distinction of patterns in feature correlations. |
Erxue Min; Da Luo; Kangyi Lin; Chunzhen Huang; Yang Liu; |
| 400 | Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a practical and theoretically grounded transition sampling approach to address action imbalance during offline RL training. |
Mila Nambiar; Supriyo Ghosh; Priscilla Ong; Yu En Chan; Yong Mong Bee; Pavitra Krishnaswamy; |
| 401 | Deep Landscape Forecasting in Multi-Slot Real-Time Bidding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we are the first to study the landscape forecasting problem in multi-slot RTB, considering the correlation between ad slots in the same pageview. |
Weitong Ou; Bo Chen; Yingxuan Yang; Xinyi Dai; Weiwen Liu; Weinan Zhang; Ruiming Tang; Yong Yu; |
| 402 | Rewiring Police Officer Training Networks to Reduce Forecasted Use of Force Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first construct a network survival model for the time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officer (FTO) to trainee. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. |
Ritika Pandey; Jeremy Carter; James Hill; George Mohler; |
| 403 | Extreme Multi-Label Classification for Ad Targeting Using Factorization Machines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Multi-Label Factorization Machine (MLFM) model, which addresses some of the challenges in XMLC problems. |
Martin Pavlovski; Srinath Ravindran; Djordje Gligorijevic; Shubham Agrawal; Ivan Stojkovic; Nelson Segura-Nunez; Jelena Gligorijevic; |
| 404 | Entity-aware Multi-task Learning for Query Understanding at Walmart Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional approaches often tackle each task separately by its own network, which leads to excessive workload for development and maintenance as well as increased latency and resource usage in large-scale E-commerce platforms. To tackle these challenges, this paper presents a multi-task learning approach to query understanding at Walmart. |
Zhiyuan Peng; Vachik Dave; Nicole McNabb; Rahul Sharnagat; Alessandro Magnani; Ciya Liao; Yi Fang; Sravanthi Rajanala; |
| 405 | Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. |
Zeyu Qin; Liuyi Yao; Daoyuan Chen; Yaliang Li; Bolin Ding; Minhao Cheng; |
| 406 | NFT-Based Data Marketplace with Digital Watermarking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The current data marketplaces cannot handle the challenges related to data ownership claims, illegal redistribution, and data ownership traceability. To overcome these problems in a general-purpose market, we propose a marketplace based on watermarking and Non-Fungible Token (NFT) technologies. |
Saeed Ranjbar Alvar; Mohammad Akbari; David (Ming Xuan) Yue; Yong Zhang; |
| 407 | Un-xPass: Measuring Soccer Player’s Creativity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is not captured by the typical result-based performance indicators, as being creative entails going beyond just doing something useful, to accomplishing something useful but in a unique or atypical way. Therefore in this paper, we define a novel metric to quantify the level of creativity involved in a player’s passes. |
Pieter Robberechts; Maaike Van Roy; Jesse Davis; |
| 408 | End-to-End Query Term Weighting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we build on top of lexical retrievers by proposing a Term Weighting BERT (TW-BERT) model. |
Karan Samel; Cheng Li; Weize Kong; Tao Chen; Mingyang Zhang; Shaleen Gupta; Swaraj Khadanga; Wensong Xu; Xingyu Wang; Kashyap Kolipaka; Michael Bendersky; Marc Najork; |
| 409 | UnifieR: A Unified Retriever for Large-Scale Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, Unifier, which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. |
Tao Shen; Xiubo Geng; Chongyang Tao; Can Xu; Guodong Long; Kai Zhang; Daxin Jiang; |
| 410 | Rover: An Online Spark SQL Tuning Service Via Generalized Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Rover, a deployed online Spark SQL tuning service for efficient and safe search on industrial workloads. |
Yu Shen; Xinyuyang Ren; Yupeng Lu; Huaijun Jiang; Huanyong Xu; Di Peng; Yang Li; Wentao Zhang; Bin Cui; |
| 411 | Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previous studies break the meaning of output logit as the click-through rate, which may lead to sub-optimal solutions. To address this issue, we propose an approach that can Jointly optimize the Ranking and Calibration abilities (JRC for short). |
Xiang-Rong Sheng; Jingyue Gao; Yueyao Cheng; Siran Yang; Shuguang Han; Hongbo Deng; Yuning Jiang; Jian Xu; Bo Zheng; |
| 412 | PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling. This paper presents a novel end-to-end re-ranking framework named PIER to tackle the above challenges which still follows the two-stage architecture and contains two mainly modules named FPSM and OCPM. |
Xiaowen Shi; Fan Yang; Ze Wang; Xiaoxu Wu; Muzhi Guan; Guogang Liao; Wang Yongkang; Xingxing Wang; Dong Wang; |
| 413 | QTNet: Theory-based Queue Length Prediction for Urban Traffic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose aQueueing-theory-based Neural Network (QTNet), which combines data-driven STGNN methods with queueing-theory-based domain knowledge of traffic engineering in order to achieve accurate and explainable predictions. |
Ryu Shirakami; Toshiya Kitahara; Koh Takeuchi; Hisashi Kashima; |
| 414 | Deep Transfer Learning for City-scale Cellular Traffic Generation Through Urban Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ADAPTIVE, a deep transfer learning framework for city-scale cellular traffic generation through the urban knowledge graph. |
Shiyuan Zhang; Tong Li; Shuodi Hui; Guangyu Li; Yanping Liang; Li Yu; Depeng Jin; Yong Li; |
| 415 | Hierarchical Reinforcement Learning for Dynamic Autonomous Vehicle Navigation at Intelligent Intersections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes NavTL, a learning-based framework to jointly control traffic signal plans and autonomous vehicle rerouting in mixed traffic scenarios where human-driven vehicles and AVs co-exist. |
Qian Sun; Le Zhang; Huan Yu; Weijia Zhang; Yu Mei; Hui Xiong; |
| 416 | TrustGeo: Uncertainty-Aware Dynamic Graph Learning for Trustworthy IP Geolocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a graph neural network (GNN)-based model, called TrustGeo, for trustworthy street-level IP geolocation. |
Wenxin Tai; Bin Chen; Fan Zhou; Ting Zhong; Goce Trajcevski; Yong Wang; Kai Chen; |
| 417 | Optimizing Airbnb Search Journey with Multi-task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. |
Chun How Tan; Austin Chan; Malay Haldar; Jie Tang; Xin Liu; Mustafa Abdool; Huiji Gao; Liwei He; Sanjeev Katariya; |
| 418 | Improving Training Stability for Multitask Ranking Models in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we share our findings and best practices we learned for improving the training stability of a real-world multitask ranking model for YouTube recommendations. |
Jiaxi Tang; Yoel Drori; Daryl Chang; Maheswaran Sathiamoorthy; Justin Gilmer; Li Wei; Xinyang Yi; Lichan Hong; Ed H. Chi; |
| 419 | Counterfactual Video Recommendation for Duration Debiasing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we eliminate the duration bias from both data and model. |
Shisong Tang; Qing Li; Dingmin Wang; Ci Gao; Wentao Xiao; Dan Zhao; Yong Jiang; Qian Ma; Aoyang Zhang; |
| 420 | Semantic-Enhanced Differentiable Search Index Inspired By Learning Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. |
Yubao Tang; Ruqing Zhang; Jiafeng Guo; Jiangui Chen; Zuowei Zhu; Shuaiqiang Wang; Dawei Yin; Xueqi Cheng; |
| 421 | Online Quality Prediction in Windshield Manufacturing Using Data-Efficient Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Production lines are subject to hardware and memory limitations and are characterized by constant changes in quality influencing factors. In this paper, we address these challenges and present an online prediction approach for real-world manufacturing processes. |
Hasan Tercan; Tobias Meisen; |
| 422 | PASS: Personalized Advertiser-aware Sponsored Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the novel problem of Personalized A dvertiser-aware Sponsored Search (PASS). |
Zhoujin Tian; Chaozhuo Li; Zhiqiang Zuo; Zengxuan Wen; Lichao Sun; Xinyue Hu; Wen Zhang; Haizhen Huang; Senzhang Wang; Weiwei Deng; Xing Xie; Qi Zhang; |
| 423 | Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta’s ads systems. |
Aditya Srinivas Timmaraju; Mehdi Mashayekhi; Mingliang Chen; Qi Zeng; Quintin Fettes; Wesley Cheung; Yihan Xiao; Manojkumar Rangasamy Kannadasan; Pushkar Tripathi; Sean Gahagan; Miranda Bogen; Rob Roudani; |
| 424 | Automatic Music Playlist Generation Via Simulation-based Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. |
Federico Tomasi; Joseph Cauteruccio; Surya Kanoria; Kamil Ciosek; Matteo Rinaldi; Zhenwen Dai; |
| 425 | Workplace Recommendation with Temporal Network Objectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on optimizing information flow, which is highly temporal and presents a number of novel algorithmic challenges. |
Kiran Tomlinson; Jennifer Neville; Longqi Yang; Mengting Wan; Cao Lu; |
| 426 | Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a stable survival analysis method for the DES sector without requiring prior knowledge of deployment environments. |
Ha Xuan Tran; Thuc Duy Le; Jiuyong Li; Lin Liu; Xiaomei Li; Jixue Liu; Tony Waters; |
| 427 | Fair Multilingual Vandalism Detection System for Wikipedia Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. |
Mykola Trokhymovych; Muniza Aslam; Ai-Jou Chou; Ricardo Baeza-Yates; Diego Saez-Trumper; |
| 428 | Auto-Validate By-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Auto-Validate-by-History (AVH) that can automatically detect DQ issues in recurring pipelines, leveraging rich statistics from historical executions. |
Dezhan Tu; Yeye He; Weiwei Cui; Song Ge; Haidong Zhang; Shi Han; Dongmei Zhang; Surajit Chaudhuri; |
| 429 | The Missing Indicator Method: From Low to High Dimensions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show empirically and theoretically that MIM improves performance for informative missing values, and we prove that MIM does not hurt linear models asymptotically for uninformative missing values. |
Mike Van Ness; Tomas M. Bosschieter; Roberto Halpin-Gregorio; Madeleine Udell; |
| 430 | Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by the real needs, in this paper, we introduce a novel framework that we developed in Amazon to maximize customer experience and control opportunity cost. |
Runzhe Wan; Yu Liu; James McQueen; Doug Hains; Rui Song; |
| 431 | A Multi-stage Framework for Online Bonus Allocation Based on Constrained User Intent Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this solution usually faces the following challenges: (1) In the user intent detection stage, due to the sparsity of interaction and noise, the traditional multi-treatment effect estimation methods lack interpretability, which may violate the domain knowledge that the marginal gain is non-negative with the increase of the bonus amount in economic theory. (2) There is an optimality gap between the two stages, which limits the upper bound of the optimal value obtained in the second stage. (3) Due to changes in the distribution of orders online, the actual cost consumption often violates the given budget limit. To solve the above challenges, we propose a framework that consists of three modules, i.e., User Intent Detection Module, Online Allocation Module, and Feedback Control Module. |
Chao Wang; Xiaowei Shi; Shuai Xu; Zhe Wang; Zhiqiang Fan; Yan Feng; An You; Yu Chen; |
| 432 | BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, by adding additional tokens, this approach increases the complexity of the learning and inference. We propose in this paper, a novel framework, BERT4CTR, that addresses these limitations. |
Dong Wang; Kavé Salamatian; Yunqing Xia; Weiwei Deng; Qi Zhang; |
| 433 | Interdependent Causal Networks for Root Cause Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization. |
Dongjie Wang; Zhengzhang Chen; Jingchao Ni; Liang Tong; Zheng Wang; Yanjie Fu; Haifeng Chen; |
| 434 | Macular: A Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by this challenge, recent works propose data augmentation or adversarial training methods to reduce the reliance on external parallel corpora. In this paper, we propose an orthogonal and novel perspective to tackle this challenging cross-lingual NLU task (i.e., when parallel corpora are unavailable). |
Haoyu Wang; Yaqing Wang; Feijie Wu; Hongfei Xue; Jing Gao; |
| 435 | ECGGAN: A Framework for Effective and Interpretable Electrocardiogram Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ECGGAN, a novel reconstruction-based ECG anomaly detection framework. |
Huazhang Wang; Zhaojing Luo; James W.L. Yip; Chuyang Ye; Meihui Zhang; |
| 436 | Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To nominate fresh contents, we built a multi-funnel nomination system that combines (i) a two-tower model with strong generalization power for coverage, and (ii) a sequence model with near real-time update on user feedback for relevance. |
Jianling Wang; Haokai Lu; Sai Zhang; Bart Locanthi; Haoting Wang; Dylan Greaves; Benjamin Lipshitz; Sriraj Badam; Ed H. Chi; Cristos J. Goodrow; Su-Lin Wu; Lexi Baugher; Minmin Chen; |
| 437 | Learning to Discover Various Simpson’s Paradoxes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods suffer from many limitations, such as being only suitable for categorical variables or one specific paradox. To address these problems, we develop a learning-based approach to discover various Simpson’s paradoxes. |
Jingwei Wang; Jianshan He; Weidi Xu; Ruopeng Li; Wei Chu; |
| 438 | Removing Camouflage and Revealing Collusion: Leveraging Gang-crime Pattern in Fraudster Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the meantime, many existing graph neural network models suffer from the challenge of extreme sample imbalance caused by rare fraudsters hidden among massive users. To handle all these challenges, in this paper, we propose a generative adversarial network framework, named Adversarial Camouflage Detector, to detect fraudsters. |
Lewen Wang; Haozhe Zhao; Cunguang Feng; Weiqing Liu; Congrui Huang; Marco Santoni; Manuel Cristofaro; Paola Jafrancesco; Jiang Bian; |
| 439 | Root Cause Analysis for Microservice Systems Via Hierarchical Reinforcement Learning from Human Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, both phases are not straightforward due to the highly dynamic and complex nature of the system, particularly in large-scale commercial architectures like Microsoft Exchange. In this paper, we propose a new framework that employs Hierarchical Reinforcement Learning from Human Feedback (HRLHF) to address these challenges. |
Lu Wang; Chaoyun Zhang; Ruomeng Ding; Yong Xu; Qihang Chen; Wentao Zou; Qingjun Chen; Meng Zhang; Xuedong Gao; Hao Fan; Saravan Rajmohan; Qingwei Lin; Dongmei Zhang; |
| 440 | ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. |
Wei-Yao Wang; Yung-Chang Huang; Tsi-Ui Ik; Wen-Chih Peng; |
| 441 | Contrastive Learning of Stress-specific Word Embedding for Social Media Based Stress Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance the performance of distinguishing words/phrases related to stressors and stressful emotions from others, in this study, we present a stress-specific word embedding learning framework upon the pre-trained language model BERT. |
Xin Wang; Huijun Zhang; Lei Cao; Kaisheng Zeng; Qi Li; Ningyun Li; Ling Feng; |
| 442 | Doctor Specific Tag Recommendation for Online Medical Record Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an efficient doctor specific tag recommendation framework for improved medical record management without side information. |
Yejing Wang; Shen Ge; Xiangyu Zhao; Xian Wu; Tong Xu; Chen Ma; Zhi Zheng; |
| 443 | Exploiting Intent Evolution in E-commercial Query Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop an intent-aware query decoder to utilize the predicted intents for suggesting the next queries. |
Yu Wang; Zhengyang Wang; Hengrui Zhang; Qingyu Yin; Xianfeng Tang; Yinghan Wang; Danqing Zhang; Limeng Cui; Monica Cheng; Bing Yin; Suhang Wang; Philip S. Yu; |
| 444 | An Empirical Study of Selection Bias in Pinterest Ads Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first investigate the data selection bias in the upper funnel (Ads Retrieval) of Pinterest’s multi-cascade ads ranking system. We then conduct comprehensive experiments to assess the performance of various state-of-the-art methods, including transfer learning, adversarial learning, and unsupervised domain adaptation. |
Yuan Wang; Peifeng Yin; Zhiqiang Tao; Hari Venkatesan; Jin Lai; Yi Fang; PJ Xiao; |
| 445 | VRDU: A Benchmark for Visually-rich Document Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we identify the desiderata for a more comprehensive benchmark and propose one we call Visually Rich Document Understanding (VRDU). |
Zilong Wang; Yichao Zhou; Wei Wei; Chen-Yu Lee; Sandeep Tata; |
| 446 | Sequence As Genes: An User Behavior Modeling Framework for Fraud Transaction Detection in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel and general user behavior pre-training framework, named Sequence As GEnes (SAGE), which provides a new perspective for user behavior modeling. |
Ziming Wang; Qianru Wu; Baolin Zheng; Junjie Wang; Kaiyu Huang; Yanjie Shi; |
| 447 | RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on a new research problem of impression pacing for preloaded ads, and propose a Reinforcement Learning To Pace framework RLTP. |
Penghui Wei; Yongqiang Chen; ShaoGuo Liu; Liang Wang; Bo Zheng; |
| 448 | DNet: Distributional Network for Distributional Individualized Treatment Effects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel architecture, called DNet, to infer distributional ITEs. |
Guojun Wu; Ge Song; Xiaoxiang Lv; Shikai Luo; Chengchun Shi; Hongtu Zhu; |
| 449 | On-device Integrated Re-ranking with Heterogeneous Behavior Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present the first on-Device Integrated Re-ranking framework, DIR, to avoid delays in processing real-time user behaviors. |
Yunjia Xi; Weiwen Liu; Yang Wang; Ruiming Tang; Weinan Zhang; Yue Zhu; Rui Zhang; Yong Yu; |
| 450 | A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design PTOCA, a Predict-Then-Optimize Couriers Allocation framework. |
Kaiwen Xia; Li Lin; Shuai Wang; Haotian Wang; Desheng Zhang; Tian He; |
| 451 | TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper (1) presents Pinterest’s ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users’ short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings. |
Xue Xia; Pong Eksombatchai; Nikil Pancha; Dhruvil Deven Badani; Po-Wei Wang; Neng Gu; Saurabh Vishwas Joshi; Nazanin Farahpour; Zhiyuan Zhang; Andrew Zhai; |
| 452 | Knowledge Based Prohibited Item Detection on Heterogeneous Risk Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, performance of these models is highly limited since domain knowledge is indispensable for identifying prohibited items but ignored by these methods. In this paper, we propose a novel Knowledge Based Prohibited item Detection system (named KBPD) to break through this limitation. |
Tingyan Xiang; Ao Li; Yugang Ji; Dong Li; |
| 453 | Graph-Aware Language Model Pre-Training on A Large Graph Corpus Can Help Multiple Graph Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, no existing study has ever investigated the pre-training of text plus graph models on large heterogeneous graphs with abundant textual information (a.k.a. large graph corpora) and then fine-tuning the model on different related downstream applications with different graph schemas. To address this problem, we propose a framework of graph-aware language model pre-training (GaLM) on a large graph corpus, which incorporates large language models and graph neural networks, and a variety of fine-tuning methods on downstream applications. |
Han Xie; Da Zheng; Jun Ma; Houyu Zhang; Vassilis N. Ioannidis; Xiang Song; Qing Ping; Sheng Wang; Carl Yang; Yi Xu; Belinda Zeng; Trishul Chilimbi; |
| 454 | QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search. |
Jian Xie; Yidan Liang; Jingping Liu; Yanghua Xiao; Baohua Wu; Shenghua Ni; |
| 455 | NEON: Living Needs Prediction System in Meituan Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the problem has not been well explored and is faced with two critical challenges. First, the needs are naturally connected to specific locations and times, suffering from complex impacts from the spatiotemporal context. Second, there is a significant gap between users’ actual living needs and their historical records on the platform. To address these two challenges, we design a system of living NEeds predictiON named NEON, consisting of three phases: feature mining, feature fusion and multi-task prediction. |
Xiaochong Lan; Chen Gao; Shiqi Wen; Xiuqi Chen; Yingge Che; Han Zhang; Huazhou Wei; Hengliang Luo; Yong Li; |
| 456 | A Data-Driven Decision Support Framework for Player Churn Analysis in Online Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we fully exploit the expertise in online games and propose a comprehensive data-driven decision support framework for addressing game player churn. |
Yu Xiong; Runze Wu; Shiwei Zhao; Jianrong Tao; Xudong Shen; Tangjie Lyu; Changjie Fan; Peng Cui; |
| 457 | PlanRanker: Towards Personalized Ranking of Train Transfer Plans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, a novel personalized deep network (Plan- Ranker) is presented in this paper to better address the problem. |
Jia Xu; Wanjie Tao; Zulong Chen; Jin Huang; Huihui Liu; Hong Wen; Shenghua Ni; Qun Dai; Yu Gu; |
| 458 | Multi-factor Sequential Re-ranking with Perception-Aware Diversification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this work proposes a general re-ranking framework named Multi-factor Sequential Re-ranking with Perception-Aware Diversification~(MPAD) to jointly optimize accuracy and diversity for feed recommendation in a sequential manner. |
Yue Xu; Hao Chen; Zefan Wang; Jianwen Yin; Qijie Shen; Dimin Wang; Feiran Huang; Lixiang Lai; Tao Zhuang; Junfeng Ge; Xia Hu; |
| 459 | Multi-channel Integrated Recommendation with Exposure Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the integrated recommendation task with exposure constraints in practical recommender systems. |
Yue Xu; Qijie Shen; Jianwen Yin; Zengde Deng; Dimin Wang; Hao Chen; Lixiang Lai; Tao Zhuang; Junfeng Ge; |
| 460 | AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a product, there are also requirements for scalability, generalizability, and explainability. To tackle these challenges, we propose AlerTiger, a deep-learning-based MLOps model monitoring system that helps AI teams across the company monitor their AI models’ health by detecting anomalies in models’ input features and output score over time. |
Zhentao Xu; Ruoying Wang; Girish Balaji; Manas Bundele; Xiaofei Liu; Leo Liu; Tie Wang; |
| 461 | Assisting Clinical Decisions for Scarcely Available Treatment Via Disentangled Latent Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel approach for individualized treatment analysis. |
Bing Xue; Ahmed Sameh Said; Ziqi Xu; Hanyang Liu; Neel Shah; Hanqing Yang; Philip Payne; Chenyang Lu; |
| 462 | Contextual Self-attentive Temporal Point Process for Physical Decommissioning Prediction of Cloud Assets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work focuses on predicting the physical decommissioning date of cloud assets as a crucial component in reverse cloud supply chain management and data center warehouse operation. |
Fangkai Yang; Jue Zhang; Lu Wang; Bo Qiao; Di Weng; Xiaoting Qin; Gregory Weber; Durgesh Nandini Das; Srinivasan Rakhunathan; Ranganathan Srikanth; Qingwei Lin; Dongmei Zhang; |
| 463 | M3PT: A Multi-Modal Model for POI Tagging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel M ulti-M odal M odel for P OI T agging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI’s textual and visual features, and the precise matching between the multi-modal representations. |
Jingsong Yang; Guanzhou Han; Deqing Yang; Jingping Liu; Yanghua Xiao; Xiang Xu; Baohua Wu; Shenghua Ni; |
| 464 | Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. |
Linxiao Yang; Rui Ren; Xinyue Gu; Liang Sun; |
| 465 | From Labels to Decisions: A Mapping-Aware Annotator Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods typically separate out the labels to decisions mapping from the modeling of annotators, leading to sub-optimal statistical inference efficiency and excessive computation complexity. We propose a novel confusion matrix model for each annotator that leverages this mapping. |
Evan Yao; Jagdish Ramakrishnan; Xu Chen; Viet-An Nguyen; Udi Weinsberg; |
| 466 | Self-supervised Classification of Clinical Multivariate Time Series Using Time Series Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel form of self-supervision: dynamics of clinical MTS. |
Yakir Yehuda; Daniel Freedman; Kira Radinsky; |
| 467 | UA-FedRec: Untargeted Attack on Federated News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the security of federated news recommendation is still unclear. In this paper, we study this problem by proposing an untargeted attack on federated news recommendation called UA-FedRec. |
Jingwei Yi; Fangzhao Wu; Bin Zhu; Jing Yao; Zhulin Tao; Guangzhong Sun; Xing Xie; |
| 468 | DGI: An Easy and Efficient Framework for GNN Model Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present DGI -a framework for easy and efficient GNN model evaluation, which automatically translates the training code of a GNN model for layer-wise evaluation to minimize user effort. |
Peiqi Yin; Xiao Yan; Jinjing Zhou; Qiang Fu; Zhenkun Cai; James Cheng; Bo Tang; Minjie Wang; |
| 469 | Learning Multivariate Hawkes Process Via Graph Recurrent Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach for modeling and predicting patterns of events in time-series learning, named graph recurrent temporal point process (GRTPP). |
Kanghoon Yoon; Youngjun Im; Jingyu Choi; Taehwan Jeong; Jinkyoo Park; |
| 470 | Group-based Fraud Detection Network on E-Commerce Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds, based on which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. |
Jianke Yu; Hanchen Wang; Xiaoyang Wang; Zhao Li; Lu Qin; Wenjie Zhang; Jian Liao; Ying Zhang; |
| 471 | Generating Synergistic Formulaic Alpha Collections Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. |
Shuo Yu; Hongyan Xue; Xiang Ao; Feiyang Pan; Jia He; Dandan Tu; Qing He; |
| 472 | LibAUC: A Deep Learning Library for X-Risk Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The contributions of this paper include: (1) It introduces a new mini-batch based pipeline for implementing DXO algorithms, which differs from existing DL pipeline in the design of controlled data samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking experiments for ablation studies and comparison with existing libraries. |
Zhuoning Yuan; Dixian Zhu; Zi-Hao Qiu; Gang Li; Xuanhui Wang; Tianbao Yang; |
| 473 | Multi Datasource LTV User Representation (MDLUR) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel user representation methodology called Multi Datasource LTV User Representation (MDLUR). |
Junwoo Yun; Wonryeol Kwak; Joohyun Kim; |
| 474 | Commonsense Knowledge Graph Towards Super APP and Its Applications in Alipay Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To deeply understand users’ needs, we propose SupKG, a commonsense knowledge graph towards Super APP to help comprehensively characterize user behaviors across different business scenarios. |
Xiaoling Zang; Binbin Hu; Jun Chu; Zhiqiang Zhang; Guannan Zhang; Jun Zhou; Wenliang Zhong; |
| 475 | Revisiting Neural Retrieval on Accelerators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We hence examine non-dot-product retrieval settings on accelerators, and propose mixture of logits (MoL), which models (user, item) similarity as an adaptive composition of elementary similarity functions. |
Jiaqi Zhai; Zhaojie Gong; Yueming Wang; Xiao Sun; Zheng Yan; Fu Li; Xing Liu; |
| 476 | Towards A Generic Framework for Mechanism-guided Deep Learning for Manufacturing Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a generic, task-agnostic MDLM framework that can embed one or more MMs in deep networks, and address the 3 aforementioned issues. |
Hanbo Zhang; Jiangxin Li; Shen Liang; Peng Wang; Themis Palpanas; Chen Wang; Wei Wang; Haoxuan Zhou; Jianwei Song; Wen Lu; |
| 477 | A Personalized Automated Bidding Framework for Fairness-aware Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, to resolve the unfairness issue and improve the overall system performance, we propose a personalized automated bidding framework, namely PerBid, shifting the classical automated bidding strategy with a unified agent to multiple context-aware agents corresponding to different advertiser clusters. |
Haoqi Zhang; Lvyin Niu; Zhenzhe Zheng; Zhilin Zhang; Shan Gu; Fan Wu; Chuan Yu; Jian Xu; Guihai Chen; Bo Zheng; |
| 478 | Understanding The Semantics of GPS-based Trajectories for Road Closure Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel road closure detection framework based on mining the semantics of trajectories, called T-Closure. |
Jiasheng Zhang; Kaiqiang An; Guoping Liu; Xiang Wen; Runbo Hu; Jie Shao; |
| 479 | GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. |
Jing Zhang; Xiaokang Zhang; Daniel Zhang-Li; Jifan Yu; Zijun Yao; Zeyao Ma; Yiqi Xu; Haohua Wang; Xiaohan Zhang; Nianyi Lin; Sunrui Lu; Juanzi Li; Jie Tang; |
| 480 | A Collaborative Transfer Learning Framework for Cross-domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, significant differences in data quantity and feature schemas between different domains, known as domain shift, may lead to negative transfer in the process of transferring. To overcome these challenges, we propose the Collaborative Cross-Domain Transfer Learning Framework (CCTL). |
Wei Zhang; Pengye Zhang; Bo Zhang; Xingxing Wang; Dong Wang; |
| 481 | Constrained Social Community Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the constrained social community recommendation problem in real applications, where each user can only join at most one community. |
Xingyi Zhang; Shuliang Xu; Wenqing Lin; Sibo Wang; |
| 482 | TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present TwHIN-BERT, a multilingual language model productionized at Twitter, trained on in-domain data from the popular social network. |
Xinyang Zhang; Yury Malkov; Omar Florez; Serim Park; Brian McWilliams; Jiawei Han; Ahmed El-Kishky; |
| 483 | Empowering Long-tail Item Recommendation Through Cross Decoupling Network (CDN) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost. |
Yin Zhang; Ruoxi Wang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Lichan Hong; James Caverlee; Ed H. Chi; |
| 484 | Towards Disentangling Relevance and Bias in Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we identify a critical issue that existing ULTR methods ignored – the bias tower can be confounded with the relevance tower via the underlying true relevance. |
Yunan Zhang; Le Yan; Zhen Qin; Honglei Zhuang; Jiaming Shen; Xuanhui Wang; Michael Bendersky; Marc Najork; |
| 485 | Modeling Dual Period-Varying Preferences for Takeaway Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in practical takeaway systems, users’ preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. |
Yuting Zhang; Yiqing Wu; Ran Le; Yongchun Zhu; Fuzhen Zhuang; Ruidong Han; Xiang Li; Wei Lin; Zhulin An; Yongjun Xu; |
| 486 | Robust Multimodal Failure Detection for Microservice Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. |
Chenyu Zhao; Minghua Ma; Zhenyu Zhong; Shenglin Zhang; Zhiyuan Tan; Xiao Xiong; LuLu Yu; Jiayi Feng; Yongqian Sun; Yuzhi Zhang; Dan Pei; Qingwei Lin; Dongmei Zhang; |
| 487 | M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The existing methods did not adequately consider the characteristics of the OTT services, i.e., rich meta information, diverse user interests, and mixed recommendation scenarios, leading to sub-optimal performance. This paper introduces the Multi-Modal Multi-Interest Multi-Scenario Matching (M5) for the OTT recommendation to fully exploit these attributes. |
Pengyu Zhao; Xin Gao; Chunxu Xu; Liang Chen; |
| 488 | JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver, suffering from high cost for multi-task deployment (e.g. a model copy for a task) and inferior performance on complex mathematical problems in practical applications. To address these issues, we propose JiuZhang 2.0, a unified Chinese PLM specially for multi-task mathematical problem solving. |
Xin Zhao; Kun Zhou; Beichen Zhang; Zheng Gong; Zhipeng Chen; Yuanhang Zhou; Ji-Rong Wen; Jing Sha; Shijin Wang; Cong Liu; Guoping Hu; |
| 489 | CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. |
Qinkai Zheng; Xiao Xia; Xu Zou; Yuxiao Dong; Shan Wang; Yufei Xue; Lei Shen; Zihan Wang; Andi Wang; Yang Li; Teng Su; Zhilin Yang; Jie Tang; |
| 490 | MIDLG: Mutual Information Based Dual Level GNN for Transaction Fraud Complaint Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they rarely consider capturing various identity-related representations and ignore the evolution of fraud ways, leading to failure in complaint verification. To address the above challenges, we propose the mutual information based dual level graph neural network, namely MIDLG, which defines a complaint as a super-node consisting of involved individuals, and characterizes the individual over node-level and super-node-level. |
Wen Zheng; Bingbing Xu; Emiao Lu; Yang Li; Qi Cao; Xuan Zong; Huawei Shen; |
| 491 | Road Planning for Slums Via Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. |
Yu Zheng; Hongyuan Su; Jingtao Ding; Depeng Jin; Yong Li; |
| 492 | Online Few-Shot Time Series Classification for Aftershock Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. |
Sheng Zhong; Vinicius M.A. Souza; Glenn Eli Baker; Abdullah Mueen; |
| 493 | PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at Alipay Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. |
Jun Zhou; Yang Bao; Daohong Jian; Hua Wu; |
| 494 | ReLoop2: Building Self-Adaptive Recommendation Models Via Responsive Error Compensation Loop Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce ReLoop2, a self-correcting learning loop that facilitates fast model adaptation in online recommender systems through responsive error compensation. |
Jieming Zhu; Guohao Cai; Junjie Huang; Zhenhua Dong; Ruiming Tang; Weinan Zhang; |
| 495 | A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. |
Xianghui Zhu; Peng Du; Shuo Shao; Chenxu Zhu; Weinan Zhang; Yang Wang; Yang Cao; |
| 496 | C-AOI: Contour-based Instance Segmentation for High-Quality Areas-of-Interest in Online Food Delivery Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method for AOI contour generation called C-AOI (Contour-based Area-of-Interest). |
Yida Zhu; Liying Chen; Daping Xiong; Shuiping Chen; Fangxiao Du; Jinghua Hao; Renqing He; Zhizhao Sun; |