Paper Digest: KDD 2019 Highlights
Download KDD-2019-Paper-Digests-Research.pdf– highlights of all 174 KDD-2019 research track papers.Download KDD-2019-Paper-Digests-DataScience.pdf– highlights of all 147 KDD-2019 applied data science track papers.
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is one of the top data mining conferences in the world. In 2019, it is to be held in Anchorage, Alaska. There were 1,808 paper submissions, of which 174 were accepted as research track papers, and 147 as applied data science track papers.
To help AI community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.
Paper Digest Team
team@paperdigest.org
TABLE 1: Research Track
| Title | Authors | Highlight | |
|---|---|---|---|
| 1 | A Free Energy Based Approach for Distance Metric Learning | Sho Inaba, Carl T. Fakhry, Rahul V. Kulkarni, Kourosh Zarringhalam | We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. |
| 2 | A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction | Qingxin Meng, Hengshu Zhu, Keli Xiao, Le Zhang, Hui Xiong | To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. |
| 3 | A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets | Pinghui Wang, Yiyan Qi, Yuanming Zhang, Qiaozhu Zhai, Chenxu Wang, John C.S. Lui, Xiaohong Guan | To solve this problem, we design a memory efficient sketch method, MaxLogHash, to accurately estimate Jaccard similarities in streaming sets. |
| 4 | A Minimax Game for Instance based Selective Transfer Learning | Bo Wang, Minghui Qiu, Xisen Wang, Yaliang Li, Yu Gong, Xiaoyi Zeng, Jun Huang, Bo Zheng, Deng Cai, Jingren Zhou | To bridge this gap, we propose a general Minimax Game based model for selective Transfer Learning (MGTL). |
| 5 | A Multiscale Scan Statistic for Adaptive Submatrix Localization | Yuchao Liu, Ery Arias-Castro | We consider the problem of localizing a submatrix with larger-than-usual entry values inside a data matrix, without the prior knowledge of the submatrix size. |
| 6 | A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health | Elias Chaibub Neto, Abhishek Pratap, Thanneer M. Perumal, Meghasyam Tummalacherla, Brian M. Bot, Lara Mangravite, Larsson Omberg | Here, instead of proposing a new method to control for confounding, we develop novel permutation based statistical tools to detect and quantify the influence of observed confounders, and estimate the unconfounded performance of the learner. |
| 7 | A Representation Learning Framework for Property Graphs | Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang | We propose PGE, a graph representation learning framework that incorporates both node and edge properties into the graph embedding procedure. |
| 8 | Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability | Yang Yang, Da-Wei Zhou, De-Chuan Zhan, Hui Xiong, Yuan Jiang | To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. |
| 9 | Adaptive Graph Guided Disambiguation for Partial Label Learning | Deng-Bao Wang, Li Li, Min-Ling Zhang | To this end, we proposed a novel approach for partial label learning based on adaptive graph guided disambiguation (PL-AGGD). |
| 10 | Adaptive Unsupervised Feature Selection on Attributed Networks | Jundong Li, Ruocheng Guo, Chenghao Liu, Huan Liu | Motivated by the sociology findings, in this work, we investigate how to harness the tie strength information embedded on the network structure to facilitate the selection of relevant nodal attributes. |
| 11 | Adaptive-Halting Policy Network for Early Classification | Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner | We design an early classification model, called EARLIEST, which tackles this multi-objective optimization problem, jointly learning (1) to classify time series and (2) at which timestep to halt and generate this prediction. |
| 12 | ADMM for Efficient Deep Learning with Global Convergence | Junxiang Wang, Fuxun Yu, Xiang Chen, Liang Zhao | In this paper, we propose a novel optimization framework for deep learning via ADMM (dlADMM) to address these challenges simultaneously. |
| 13 | Adversarial Learning on Heterogeneous Information Networks | Binbin Hu, Yuan Fang, Chuan Shi | Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN for HIN embedding, which trains both a discriminator and a generator in a minimax game. |
| 14 | Adversarial Substructured Representation Learning for Mobile User Profiling | Pengyang Wang, Yanjie Fu, Hui Xiong, Xiaolin Li | Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. |
| 15 | Adversarial Variational Embedding for Robust Semi-supervised Learning | Xiang Zhang, Lina Yao, Feng Yuan | To address the aforementioned issues, we propose a novel Adversarial Variational Embedding (AVAE) framework for robust and effective semi-supervised learning to leverage both the advantage of GAN as a high quality generative model and VAE as a posterior distribution learner. |
| 16 | Adversarially Robust Submodular Maximization under Knapsack Constraints | Dmitrii Avdiukhin, Slobodan Mitrovic, Grigory Yaroslavtsev, Samson Zhou | We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings. |
| 17 | An Visual Dialog Augmented Interactive Recommender System | Tong Yu, Yilin Shen, Hongxia Jin | In this paper, we propose a novel dialog-based recommender system to interactively recommend a list of items with visual appearance. |
| 18 | Assessing The Factual Accuracy of Generated Text | Ben Goodrich, Vinay Rao, Peter J. Liu, Mohammad Saleh | We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. |
| 19 | AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization | Cong Fu, Yonghui Zhang, Deng Cai, Xiang Ren | To address the aforementioned problems, we propose a novel visualization approach named as Anchor-t-SNE (AtSNE), which provides efficient GPU-based visualization solution for large-scale and high-dimensional data. |
| 20 | Attribute-Driven Backbone Discovery | Sheng Guan, Hanchao Ma, Yinghui Wu | This paper introduces a novel class of attributed backbones and detection algorithms in richly attributed networks. |
| 21 | Auditing Data Provenance in Text-Generation Models | Congzheng Song, Vitaly Shmatikov | To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we develop a new model auditing technique that helps users check if their data was used to train a machine learning model. |
| 22 | Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning | Kunpeng Liu, Yanjie Fu, Pengfei Wang, Le Wu, Rui Bo, Xiaolin Li | In this paper, we propose a multi-agent reinforcement learning framework for the feature selection problem. |
| 23 | AutoNE: Hyperparameter Optimization for Massive Network Embedding | Ke Tu, Jianxin Ma, Peng Cui, Jian Pei, Wenwu Zhu | In this paper, we propose a novel framework, named AutoNE, to automatically optimize the hyperparameters of a NE algorithm on massive networks. |
| 24 | Axiomatic Interpretability for Multiclass Additive Models | Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana | Axiomatic Interpretability for Multiclass Additive Models |
| 25 | Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction | Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang | In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. |
| 26 | Certifiable Robustness and Robust Training for Graph Convolutional Networks | Daniel Z?gner, Stephan G?nnemann | We propose the first method for certifiable (non-)robustness of graph convolutional networks with respect to perturbations of the node attributes. |
| 27 | Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks | Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh | In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). |
| 28 | Clustering without Over-Representation | Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian | In this paper we consider clustering problems in which each point is endowed with a color. |
| 29 | Conditional Random Field Enhanced Graph Convolutional Neural Networks | Hongchang Gao, Jian Pei, Heng Huang | To address this issue, we propose a novel CRF layer for graph convolutional neural networks to encourage similar nodes to have similar hidden features. |
| 30 | Contextual Fact Ranking and Its Applications in Table Synthesis and Compression | Silu Huang, Jialu Liu, Flip Korn, Xuezhi Wang, You Wu, Dale Markowitz, Cong Yu | In particular, we develop pointwise and pair-wise ranking models, using textual and statistical information for the given entities and context derived from their sources. |
| 31 | Contrastive Antichains in Hierarchies | Anes Bendimerad, Jefrey Lijffijt, Marc Plantevit, C?line Robardet, Tijl De Bie | In the present paper, we attempt to characterize such concepts in terms of so-called contrastive antichains: particular kinds of subsets of their attributes and their values. |
| 32 | Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network | Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Xinran Tong, Hui Xiong | Along this line, this paper provides a novel Co-prediction method based on Spatio-Temporal neural Network, namely, CoST-Net. |
| 33 | Coresets for Minimum Enclosing Balls over Sliding Windows | Yanhao Wang, Yuchen Li, Kian-Lee Tan | This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream. |
| 34 | CoSTCo: A Neural Tensor Completion Model for Sparse Tensors | Hanpeng Liu, Yaguang Li, Michael Tsang, Yan Liu | We propose a novel convolutional neural network (CNN) based model, named CoSTCo (Convolutional Sparse Tensor Completion). |
| 35 | Coupled Variational Recurrent Collaborative Filtering | Qingquan Song, Shiyu Chang, Xia Hu | To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem. |
| 36 | DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation | Donghua Liu, Jing Li, Bo Du, Jun Chang, Rong Gao | Therefore, we propose a novel d ual a ttention m utual l earning between ratings and reviews for item recommendation, named DAML. |
| 37 | Deep Anomaly Detection with Deviation Networks | Guansong Pang, Chunhua Shen, Anton van den Hengel | This paper introduces a novel anomaly detection framework and its instantiation to address these problems. |
| 38 | Deep Landscape Forecasting for Real-time Bidding Advertising | Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Yong Yu | In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. |
| 39 | Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information | Maya Okawa, Tomoharu Iwata, Takeshi Kurashima, Yusuke Tanaka, Hiroyuki Toda, Naonori Ueda | In this paper, we propose DMPP (Deep Mixture Point Processes), a point process model for predicting spatio-temporal events with the use of rich contextual information; a key advance is its incorporation of the heterogeneous and high-dimensional context available in image and text data. |
| 40 | DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks | Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian, Tie-Yan Liu | In this paper, we propose a new learning framework, DeepGBM, which integrates the advantages of the both NN and GBDT by using two corresponding NN components: (1) CatNN, focusing on handling sparse categorical features. |
| 41 | dEFEND: Explainable Fake News Detection | Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, Huan Liu | In this paper, therefore, we study the explainable detection of fake news. |
| 42 | DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification | Jun Wu, Jingrui He, Jiejun Xu | To address these problems, we propose a generic degree-specific graph neural network named DEMO-Net motivated by Weisfeiler-Lehman graph isomorphism test that recursively identifies 1-hop neighborhood structures. |
| 43 | Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction | Jing-Han Wu, Min-Ling Zhang | In this paper, the first attempt towards partial label dimensionality reduction is investigated by endowing the popular linear discriminant analysis (LDA) techniques with the ability of dealing with partial label training examples. |
| 44 | Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models | Michael Doron, Idan Segev, Dafna Shahaf | In this work, we present HINT (Hessian INTerestingness) — a new algorithm that can automatically and systematically explore black-box models and highlight local nonlinear interactions in the input-output space of the model. |
| 45 | Dual Averaging Method for Online Graph-structured Sparsity | Baojian Zhou, Feng Chen, Yiming Ying | To address these limitations, in this paper we propose a new algorithm for graph-structured sparsity constraint problems under online setting, which we call GraphDA. |
| 46 | Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination | Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, Guihai Chen | The difference is that the former deals with users’ histories of clicked items, while the latter focuses on items’ histories of infected users.In this paper, we take a fresh view and propose dual sequential prediction models that unify these two thinking paradigms. |
| 47 | Dynamic Modeling and Forecasting of Time-evolving Data Streams | Yasuko Matsubara, Yasushi Sakurai | We present an intuitive model, namely OrbitMap, which provides a good summary of time-series evolution in streams. |
| 48 | Dynamical Origins of Distribution Functions | Chengxi Zang, Peng Cui, Wenwu Zhu, Fei Wang | In this paper, we try to model these time-evolving phenomena by a dynamic system and the data sets observed at different time stamps are probability distribution functions generated by such a dynamic system. |
| 49 | EdMot: An Edge Enhancement Approach for Motif-aware Community Detection | Pei-Zhen Li, Ling Huang, Chang-Dong Wang, Jian-Huang Lai | To address the above fragmentation issue, we propose an Edge enhancement approach for Motif-aware community detection (EdMot ). |
| 50 | Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation | Lisi Chen, Shuo Shang, Christian S. Jensen, Bin Yao, Zhiwei Zhang, Ling Shao | To this end, we propose a novel parallel split-and-combine approach to enable route search by locations (RSL-Psc). |
| 51 | Effective and Efficient Sports Play Retrieval with Deep Representation Learning | Zheng Wang, Cheng Long, Gao Cong, Ce Ju | To this end, we propose a deep learning approach to learn the representations of sports plays, called play2vec, which is robust against noise and takes only linear time to compute the similarity between two sports plays. |
| 52 | Efficient and Effective Express via Contextual Cooperative Reinforcement Learning | Yexin Li, Yu Zheng, Qiang Yang | Considering this problem, we propose a reinforcement learning based framework to learn a courier management policy. |
| 53 | Efficient Global String Kernel with Random Features: Beyond Counting Substructures | Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal | In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples. |
| 54 | Efficient Maximum Clique Computation over Large Sparse Graphs | Lijun Chang | In this paper, we design an algorithm MC-BRB which transforms an instance of MCC-Sparse to instances of k-clique finding over dense subgraphs (KCF-Dense) that can be computed by the existing MCC-Dense solvers. |
| 55 | Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation | Jingyuan Wang, Ning Wu, Wayne Xin Zhao, Fanzhang Peng, Xin Lin | To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task. |
| 56 | Enhancing Collaborative Filtering with Generative Augmentation | Qinyong Wang, Hongzhi Yin, Hao Wang, Quoc Viet Hung Nguyen, Zi Huang, Lizhen Cui | In light of these challenges, we propose a generic and effective CF model called AugCF that supports a wide variety of recommendation tasks. |
| 57 | Enhancing Domain Word Embedding via Latent Semantic Imputation | Shibo Yao, Dantong Yu, Keli Xiao | We present a novel method named Latent Semantic Imputation (LSI) to transfer external knowledge into semantic space for enhancing word embedding. |
| 58 | Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation | Wenjie Shang, Yang Yu, Qingyang Li, Zhiwei Qin, Yiping Meng, Jieping Ye | In this paper, by treating the hidden confounder as a hidden policy, we propose a deconfounded multi-agent environment reconstruction (DEMER) approach in order to learn the environment together with the hidden confounder. |
| 59 | EpiDeep: Exploiting Embeddings for Epidemic Forecasting | Bijaya Adhikari, Xinfeng Xu, Naren Ramakrishnan, B. Aditya Prakash | We propose EpiDeep, a novel deep neural network approach for epidemic forecasting which tackles all of these issues by learning meaningful representations of incidence curves in a continuous feature space and accurately predicting future incidences, peak intensity, peak time, and onset of the upcoming season. |
| 60 | Estimating Graphlet Statistics via Lifting | Kirill Paramonov, Dmitry Shemetov, James Sharpnack | We introduce a framework for estimating the graphlet count—the number of occurrences of a small subgraph motif (e.g. a wedge or a triangle) in the network. |
| 61 | Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks | Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos | In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. |
| 62 | ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data | Songshan Yang, Jiawei Wen, Xiang Zhan, Daniel Kifer | Motivated by these ideas, we propose a new method using pseudo-features to obtain an ideal tuning parameter. |
| 63 | Exact-K Recommendation via Maximal Clique Optimization | Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu | To tackle this specific combinatorial optimization problem which is NP-hard, we propose Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a decoder with attention mechanism. |
| 64 | Exploiting Cognitive Structure for Adaptive Learning | Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang | To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. |
| 65 | Factorization Bandits for Online Influence Maximization | Qingyun Wu, Zhige Li, Huazheng Wang, Wei Chen, Hongning Wang | In this paper, we study the problem of online influence maximization in social networks. |
| 66 | Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach | Minji Yoon, Bryan Hooi, Kijung Shin, Christos Faloutsos | In this work, we propose AnomRank, an online algorithm for anomaly detection in dynamic graphs. |
| 67 | Fast Approximation of Empirical Entropy via Subsampling | Chi Wang, Bailu Ding | We develop algorithms to progressively subsample the dataset and return correct answers with high probability. |
| 68 | Fates of Microscopic Social Ecosystems: Keep Alive or Dead? | Haoyang Li, Peng Cui, Chengxi Zang, Tianyang Zhang, Wenwu Zhu, Yishi Lin | In this paper, rather than studying social ecosystems at the population level, we analyze the fates of different microscopic social ecosystems, namely the final states of their collective activity dynamics in a real-world online social media with detailed individual level records for the first time. |
| 69 | Fighting Opinion Control in Social Networks via Link Recommendation | Victor Amelkin, Ambuj K. Singh | In this work, we assume that the adversary aims to maliciously change the network’s average opinion by altering the opinions of some unknown users. We, then, state an NP-hard problem of disabling such opinion control attempts via strategically altering the network’s users’ eigencentralities by recommending a limited number of links to the users. |
| 70 | Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging | Nikita Klyuchnikov, Davide Mottin, Georgia Koutrika, Emmanuel M?ller, Panagiotis Karras | In this paper, we propose MF-ASC, a novel active search mechanism that performs well with minimal user input. |
| 71 | Focused Context Balancing for Robust Offline Policy Evaluation | Hao Zou, Kun Kuang, Boqi Chen, Peixuan Chen, Peng Cui | In this paper, we propose a non-parametric method, named Focused Context Balancing (FCB) algorithm, to learn sample weights for context balancing, so that the distribution shift induced by the past policy and new policy can be eliminated respectively. |
| 72 | GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization | Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, Panos Kalnis | In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. |
| 73 | Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space | Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed | In this paper, we present an approach for hierarchical clustering that searches over continuous representations of trees in hyperbolic space by running gradient descent. |
| 74 | Graph Convolutional Networks with EigenPooling | Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang | In this paper, we introduce a pooling operator $\pooling$ based on graph Fourier transform, which can utilize the node features and local structures during the pooling process. |
| 75 | Graph Recurrent Networks With Attributed Random Walks | Xiao Huang, Qingquan Song, Yuening Li, Xia Hu | To bridge the gap, we explore to perform joint random walks on attributed networks, and utilize them to boost the deep node representation learning. |
| 76 | Graph Representation Learning via Hard and Channel-Wise Attention Networks | Hongyang Gao, Shuiwang Ji | In this work, we propose novel hard graph attention operator~(hGAO) and channel-wise graph attention operator~(cGAO). |
| 77 | Graph Transformation Policy Network for Chemical Reaction Prediction | Kien Do, Truyen Tran, Svetha Venkatesh | To this end, we propose Graph Transformation Policy Network (GTPN) – a novel generic method that combines the strengths of graph neural networks and reinforcement learning to learn reactions directly from data with minimal chemical knowledge. |
| 78 | Graph-based Semi-Supervised & Active Learning for Edge Flows | Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson | We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. |
| 79 | GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data | Yujun Yan, Jiong Zhu, Marlena Duda, Eric Solarz, Chandra Sripada, Danai Koutra | In this work focusing on fMRI-derived brain graphs, a modality that partially handles some challenges of fMRI data, we propose a grouping-based interpretable neural network model, GroupINN, that effectively classifies cognitive performance with 85% fewer model parameters than baseline deep models, while also identifying the most predictive brain subnetworks within several task-specific contexts. |
| 80 | HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings | Changping Meng, Jiasen Yang, Bruno Ribeiro, Jennifer Neville | In this work, we develop a deep neural network framework to learn inductive SoS embeddings that are invariant to SoS permutations. |
| 81 | Heterogeneous Graph Neural Network | Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla | In this paper, we propose HetGNN, a heterogeneous graph neural network model, to resolve this issue. |
| 82 | Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications | Zhe Jiang, Arpan Man Sainju | To fill the gap, this paper proposes a novel spatial structured model called hidden Markov contour tree (HMCT), which generalizes the traditional hidden Markov model from a total order sequence to a partial order polytree. |
| 83 | Hidden POI Ranking with Spatial Crowdsourcing | Yue Cui, Liwei Deng, Yan Zhao, Bin Yao, Vincent W. Zheng, Kai Zheng | In this work, we investigate how to eliminate the hidden feature of H-POIs by enhancing conventional crowdsourced ranking aggregation framework with heterogeneous (i.e., H-POI and Popular Point of Interest (P-POI)) pairwise tasks. |
| 84 | Hierarchical Gating Networks for Sequential Recommendation | Chen Ma, Peng Kang, Xue Liu | To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. |
| 85 | Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction | Hongliang Fei, Shulong Tan, Ping Li | In this paper, we focus on medical domain and propose an automatic way to accelerate the process of medical synonymy resource development for Chinese, including both formal entities from healthcare professionals and noisy descriptions from end-users. Furthermore, we create a large medical text corpus in Chinese that includes annotations for entities, descriptions and synonymous pairs for future research in this direction. |
| 86 | Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts | Kishlay Jha, Guangxu Xun, Yaqing Wang, Aidong Zhang | To answer these questions, in this study, we present a novel HG framework that unearths the latent associations between concepts by modeling their co-evolution across complementary sources of information. |
| 87 | Identifiability of Cause and Effect using Regularized Regression | Alexander Marx, Jilles Vreeken | In this paper we show under which general conditions we can identify cause from effect by simply choosing the direction with the best regression score. |
| 88 | Improving the Quality of Explanations with Local Embedding Perturbations | Yunzhe Jia, James Bailey, Kotagiri Ramamohanarao, Christopher Leckie, Michael E. Houle | To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. |
| 89 | Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis | Weiyu Cheng, Yanyan Shen, Linpeng Huang, Yanmin Zhu | Inspired by this, we propose a novel explanation method named FIA (Fast Influence Analysis) to understand the prediction of trained LFMs by tracing back to the training data with influence functions. |
| 90 | Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding | Zhige Li, Derek Yang, Li Zhao, Jiang Bian, Tao Qin, Tie-Yan Liu | To address this problem, in this paper, we design a Technical Trading Indicator Optimization(TTIO) framework that manages to optimize the original technical indicator by leveraging stock-wise properties. |
| 91 | Interpretable and Steerable Sequence Learning via Prototypes | Yao Ming, Panpan Xu, Huamin Qu, Liu Ren | We propose ProSeNet, an interpretable and steerable deep sequence model with natural explanations derived from case-based reasoning. |
| 92 | Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories | Rui Yan, Ran Le, Yang Song, Tao Zhang, Xiangliang Zhang, Dongyan Zhao | To this end, in this paper, we propose a novel matching network with preference modeled. |
| 93 | Investigating Cognitive Effects in Session-level Search User Satisfaction | Mengyang Liu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma | In this paper, we collect a dataset through a laboratory study in which users need to complete some complex search tasks. With the help of hierarchical linear models (HLM), we try to reveal how user’s query-level and session-level satisfaction are affected by different cognitive effects. |
| 94 | Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding | Ninghao Liu, Qiaoyu Tan, Yuening Li, Hongxia Yang, Jingren Zhou, Xia Hu | In this paper, we propose a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling. |
| 95 | Isolation Set-Kernel and Its Application to Multi-Instance Learning | Bi-Cun Xu, Kai Ming Ting, Zhi-Hua Zhou | We introduce Isolation Set-Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning. |
| 96 | KGAT: Knowledge Graph Attention Network for Recommendation | Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua | In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We release the codes and datasets at https://github.com/xiangwang1223/knowledge_graph_attention_network. |
| 97 | K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters | Feiping Nie, Cheng-Long Wang, Xuelong Li | In this paper, we make an extension of K-means for the clustering of multiple means. |
| 98 | Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems | Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang | Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations. |
| 99 | ?Opt: Learn to Regularize Recommender Models in Finer Levels | Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, Yue Wang | In this paper, we propose a hyperparameter optimization method, lambdaOpt, which automatically and adaptively enforces regularization during training. |
| 100 | Latent Network Summarization: Bridging Network Embedding and Summarization | Di Jin, Ryan A. Rossi, Eunyee Koh, Sungchul Kim, Anup Rao, Danai Koutra | We propose Multi-LENS, an inductive multi-level latent network summarization approach that leverages a set of relational operators and relational functions (compositions of operators) to capture the structure of egonets and higher-order subgraphs, respectively. |
| 101 | Learning Class-Conditional GANs with Active Sampling | Ming-Kun Xie, Sheng-Jun Huang | In this paper, we propose an active sampling method to reduce the labeling cost for effectively training the class-conditional GANs. |
| 102 | Learning Dynamic Context Graphs for Predicting Social Events | Songgaojun Deng, Huzefa Rangwala, Yue Ning | In this paper, we study graph representations in modeling social events to identify dynamic properties of event contexts as social indicators. |
| 103 | Learning from Incomplete and Inaccurate Supervision | Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, Zhi-Hua Zhou | In this paper, we consider the problem of learning from incomplete and inaccurate supervision, where only a limited subset of training data is labeled but potentially with noise. |
| 104 | Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining | Tomoki Yoshida, Ichiro Takeuchi, Masayuki Karasuyama | We propose a novel supervised metric learning method for a subgraph-based distance, called interpretable graph metric learning (IGML). |
| 105 | Learning Network-to-Network Model for Content-rich Network Embedding | Zhicheng He, Jie Liu, Na Li, Yalou Huang | In this paper, we consider the representation learning problem for content-rich networks whose nodes are associated with rich content information. |
| 106 | Link Prediction with Signed Latent Factors in Signed Social Networks | Pinghua Xu, Wenbin Hu, Jia Wu, Bo Du | Hence, in this paper, we propose a s igned l atent f actor (SLF) model that answers both these questions and, additionally, considers four types of relationships: positive, negative, neutral and no relationship at all. |
| 107 | Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit | Zhiqiang Tao, Sheng Li, Zhaowen Wang, Chen Fang, Longqi Yang, Handong Zhao, Yun Fu | To address these challenges, we propose a Log2Intent framework for interpretable user modeling in this paper. |
| 108 | MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network | Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su | To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. |
| 109 | MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation | Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung | This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. |
| 110 | Mining Algorithm Roadmap in Scientific Publications | Hanwen Zha, Wenhu Chen, Keqian Li, Xifeng Yan | To accelerate such a process, we first define a new problem called mining algorithm roadmap in scientific publications, and then propose a new weakly supervised method to build the roadmap. |
| 111 | MinJoin: Efficient Edit Similarity Joins via Local Hash Minima | Haoyu Zhang, Qin Zhang | We study the problem of computing similarity joins under edit distance on a set of strings. |
| 112 | Modeling Dwell Time Engagement on Visual Multimedia | Hemank Lamba, Neil Shah | For instance, how can we model engagement for a specific content or viewer sample, and across multiple samples? Can we model and discover patterns in these interactions, and detect outlying behaviors corresponding to abnormal engagement? In this paper, we study these questions in depth. |
| 113 | Modeling Extreme Events in Time Series Prediction | Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He | In this paper, we explore the central theme of improving the ability of deep learning on modeling extreme events for time series prediction. |
| 114 | Multiple Relational Attention Network for Multi-task Learning | Jiejie Zhao, Bowen Du, Leilei Sun, Fuzhen Zhuang, Weifeng Lv, Hui Xiong | Along this line, we propose aMultiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. |
| 115 | Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings | Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme | In this paper, we aim to overcome these two main drawbacks by proposing a flexible nonlinear latent embedding model (BRNLE) for the classification of multi-relational data. |
| 116 | Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification | Chang Li, Dongjin Song, Dacheng Tao | Here, we present a multi-task recurrent neural network (RNN) with high-order Markov random fields (MRFs) to predict stock price movement direction. |
| 117 | Network Density of States | Kun Dong, Austin R. Benson, David Bindel | In this paper, we delve into the heart of spectral densities of real-world graphs. |
| 118 | NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching | Dingqi Yang, Paolo Rosso, Bin Li, Philippe Cudre-Mauroux | To address these issues, we propose NodeSketch, a highly-efficient graph embedding technique preserving high-order node proximity via recursive sketching. |
| 119 | OBOE: Collaborative Filtering for AutoML Model Selection | Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine Udell | This paper introduces OBOE, a collaborative filtering method for time-constrained model selection and hyperparameter tuning. |
| 120 | Off-policy Learning for Multiple Loggers | Li He, Long Xia, Wei Zeng, Zhi-Ming Ma, Yihong Zhao, Dawei Yin | Motivated by this, in this paper, we investigate off-policy learning when the training data coming from multiple historical policies. |
| 121 | On Dynamic Network Models and Application to Causal Impact | Yu-Chia Chen, Avleen S. Bijral, Juan Lavista Ferres | In this paper we present a conditional pseudo-likelihood based extension to dynamic SBM that can be efficiently estimated by optimizing a regularized objective. |
| 122 | Optimizing Impression Counts for Outdoor Advertising | Yipeng Zhang, Yuchen Li, Zhifeng Bao, Songsong Mo, Ping Zhang | In this paper we propose and study the problem of optimizing the influence of outdoor advertising (ad) when impression counts are taken into consideration. |
| 123 | Optimizing Peer Learning in Online Groups with Affinities | Mohammadreza Esfandiari, Dong Wei, Sihem Amer-Yahia, Senjuti Basu Roy | We propose principled modeling of these problems and investigate theoretical and algorithmic challenges. |
| 124 | Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling | Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, Kai Zheng | To solve the problem effectively, we propose a unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively. |
| 125 | Pairwise Comparisons with Flexible Time-Dynamics | Lucas Maystre, Victor Kristof, Matthias Grossglauser | Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics. |
| 126 | Paper Matching with Local Fairness Constraints | Ari Kobren, Barna Saha, Andrew McCallum | In this paper, we propose a novel local fairness formulation of paper matching that directly addresses both of these issues. |
| 127 | PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data | Sanjoy Dey, Ping Zhang, Daby Sow, Kenney Ng | In this paper, we propose a unified computational framework, called PerDREP, to predict the unique response patterns of each individual patient from EHR data. |
| 128 | Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks | Srijan Kumar, Xikun Zhang, Jure Leskovec | Here we propose JODIE, a coupled recurrent neural network model that learns the embedding trajectories of users and items. |
| 129 | Predicting Path Failure In Time-Evolving Graphs | Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan | In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. |
| 130 | PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network | Hua Wei, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash Gayah, Kai Xu, Zhenhui Li | To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. |
| 131 | PrivPy: General and Scalable Privacy-Preserving Data Mining | Yi Li, Wei Xu | We present multi-party computation (MPC) framework designed for large-scale data mining tasks. |
| 132 | ProGAN: Network Embedding via Proximity Generative Adversarial Network | Hongchang Gao, Jian Pei, Heng Huang | To address this problem, in this paper, we propose a novel proximity generative adversarial network (ProGAN) which can generate proximities. |
| 133 | Quantifying Long Range Dependence in Language and User Behavior to improve RNNs | Francois Belletti, Minmin Chen, Ed H. Chi | We propose a principled estimation procedure of LRD in sequential datasets based on established LRD theory for real-valued time series and apply it to sequences of symbols with million-item-scale dictionaries. |
| 134 | QuesNet: A Unified Representation for Heterogeneous Test Questions | Yu Yin, Qi Liu, Zhenya Huang, Enhong Chen, Wei Tong, Shijin Wang, Yu Su | To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. |
| 135 | Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions | Souhaib Ben Taieb, Bonsoo Koo | We propose a new forecasting method which relaxes these unbiasedness conditions, and seeks the revised forecasts with the best tradeoff between bias and forecast variance. |
| 136 | Relation Extraction via Domain-aware Transfer Learning | Shimin Di, Yanyan Shen, Lei Chen | In this paper, we propose a novel approach called, Relation Extraction via Domain-aware Transfer Learning (ReTrans), to extract relation mentions from a given text corpus by exploring the experience from a large amount of existing KBs which may not be closely related to the target relation. |
| 137 | Representation Learning for Attributed Multiplex Heterogeneous Network | Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang | In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. |
| 138 | Retaining Privileged Information for Multi-Task Learning | Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, Li-wei H. Lehman | In this work, we present a LUPI formulation that allows privileged information to be retained in a multi-task learning setting. |
| 139 | Revisiting kd-tree for Nearest Neighbor Search | Parikshit Ram, Kaushik Sinha | In the article, we build upon randomized-partition trees \citedasgupta2013randomized to propose \kdtree based approximate search schemes with $O(d log d + log n)$ query time for data sets with n points in d dimensions and rigorous theoretical guarantees on the search accuracy. |
| 140 | Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering | Jie Zhao, Ziyu Guan, Huan Sun | In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. |
| 141 | Robust Graph Convolutional Networks Against Adversarial Attacks | Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu | To address this problem, we propose Robust GCN (RGCN), a novel model that “fortifies” GCNs against adversarial attacks. |
| 142 | Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning | Yaqiang Yao, Jie Cao, Huanhuan Chen | In this paper, we propose a robust clustered multi-task learning approach that clusters tasks into several groups by learning the representative tasks. |
| 143 | Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding | Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu Aggarwal | In this paper, we propose a new family of global alignment graph kernels, which take into account the global properties of graphs by using geometric node embeddings and an associated node transportation based on earth mover’s distance. |
| 144 | Scalable Graph Embeddings via Sparse Transpose Proximities | Yuan Yin, Zhewei Wei | We propose transpose proximity, a unified approach that solves both problems. |
| 145 | Scalable Hierarchical Clustering with Tree Grafting | Nicholas Monath, Ari Kobren, Akshay Krishnamurthy, Michael R. Glass, Andrew McCallum | We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. |
| 146 | Scaling Multi-Armed Bandit Algorithms | Edouard Fouch?, Junpei Komiyama, Klemens B?hm | In this paper, we present a variant of the problem, which we call the Scaling MAB (S-MAB): The goal of the decision maker is not only to maximize the cumulative rewards, i.e., choosing the arms with the highest expected reward, but also to decide how many arms to select so that, in expectation, the cost of selecting arms does not exceed the rewards. |
| 147 | Scaling Multinomial Logistic Regression via Hybrid Parallelism | Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S.V.N. Vishwanathan | To overcome this problem, we propose a reformulation of the original objective that exploits double-separability, an attractive property that naturally leads to hybrid parallelism. |
| 148 | Separated Trust Regions Policy Optimization Method | Luobao Zou, Zhiwei Zhuang, Yin Cheng, Xuechun Wang, Weidong Zhang | In this work, we propose a moderate policy update method for reinforcement learning, which encourages the agent to explore more boldly in early episodes but updates the policy more cautious. |
| 149 | Sequential Anomaly Detection using Inverse Reinforcement Learning | Min-hwan Oh, Garud Iyengar | We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent’s underlying function which triggers his/her behavior. |
| 150 | Sets2Sets: Learning from Sequential Sets with Neural Networks | Haoji Hu, Xiangnan He | In this paper, we formulate this problem as a sequential sets to sequential sets learning problem. |
| 151 | Sherlock: A Deep Learning Approach to Semantic Data Type Detection | Madelon Hulsebos, Kevin Hu, Michiel Bakker, Emanuel Zgraggen, Arvind Satyanarayan, Tim Kraska, ?agatay Demiralp, C?sar Hidalgo | We introduce Sherlock, a multi-input deep neural network for detecting semantic types. |
| 152 | Significance of Patterns in Data Visualisations | Rafael Savvides, Andreas Henelius, Emilia Oikarinen, Kai Puolam?ki | In this paper we consider the following important problem: when we explore data visually and observe patterns, how can we determine their statistical significance? |
| 153 | Social Recommendation with Optimal Limited Attention | Xin Wang, Wenwu Zhu, Chenghao Liu | We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. |
| 154 | SPuManTE: Significant Pattern Mining with Unconditional Testing | Leonardo Pellegrina, Matteo Riondato, Fabio Vandin | We present SPuManTE, an efficient algorithm for mining significant patterns from a transactional dataset. |
| 155 | Stability and Generalization of Graph Convolutional Neural Networks | Saurabh Verma, Zhi-Li Zhang | In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting. |
| 156 | State-Sharing Sparse Hidden Markov Models for Personalized Sequences | Hongzhi Shi, Chao Zhang, Quanming Yao, Yong Li, Funing Sun, Depeng Jin | We address this challenge by proposing a state-sharing sparse hidden Markov model (S3HMM) that can uncover personalized sequential patterns without suffering from data scarcity. |
| 157 | Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units | Prathamesh Deshpande, Sunita Sarawagi | We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep globally trained time-series forecasting models. |
| 158 | Streaming Session-based Recommendation | Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, Nguyen Quoc Viet Hung | In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. |
| 159 | SurfCon: Synonym Discovery on Privacy-Aware Clinical Data | Zhen Wang, Xiang Yue, Soheil Moosavinasab, Yungui Huang, Simon Lin, Huan Sun | In this paper, we study a new setting named synonym discovery on privacy-aware clinical data (i.e., medical terms extracted from the clinical texts and their aggregated co-occurrence counts, without raw clinical texts). |
| 160 | Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning | Shuyang Yu, Bin Gu, Kunpeng Ning, Haiyan Chen, Jian Pei, Heng Huang | To fill this gap, in this paper, we propose a new incremental S3VM algorithm (IL-BCS3VM) based on IL-S3VM which can effectively handle the balancing constraint and directly update the solution of BCS3VM. |
| 161 | Task-Adversarial Co-Generative Nets | Pei Yang, Qi Tan, Hanghang Tong, Jingrui He | In this paper, we propose Task-Adversarial co-Generative Nets (TAGN) for learning from multiple tasks. |
| 162 | Tensorized Determinantal Point Processes for Recommendation | Romain Warlop, J?r?mie Mary, Mike Gartrell | We present an enhanced DPP model that is specialized for the task of basket completion, the tensorized DPP. |
| 163 | Testing Dynamic Incentive Compatibility in Display Ad Auctions | Yuan Deng, Sebastien Lahaie | Motivated by this concern, this paper takes the perspective of a single advertiser and develops statistical tests to confirm whether an underlying auction mechanism is dynamically incentive compatible (IC), so that truthful bidding in each individual auction and across time is an optimal strategy. |
| 164 | The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach | Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, Hui Xiong | To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. |
| 165 | The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model | Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang | In this work, we propose a novel representation of SciKG, which has three layers. |
| 166 | Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms | Boyang Li, Yurong Cheng, Ye Yuan, Guoren Wang, Lei Chen | Thus, in this paper, we propose a 3-Dimensional Stable Spatial Matching(3D-SSM) for the 3D matching problem innew SC services. |
| 167 | Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios | Stefano Giovanni Rizzo, Giovanna Vantini, Sanjay Chawla | This paper addresses the traffic light control problem in a complex scenario, such as a signalized roundabout with heavy traffic volumes, with the aim of maximizing throughput and avoiding traffic jams. |
| 168 | Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? | Xiaowei Jia, Sheng Li, Handong Zhao, Sungchul Kim, Vipin Kumar | To this end, we develop a novel adversarial training approach for sequential data classification by investigating when and how to perturb a sequence for an effective data augmentation. |
| 169 | Training and Meta-Training Binary Neural Networks with Quantum Computing | Abdulah Fawaz, Paul Klein, Sebastien Piat, Simone Severini, Peter Mountney | We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. |
| 170 | TUBE: Embedding Behavior Outcomes for Predicting Success | Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang | In this work, we define a measurement of behavior outcomes, which forms a test tube-shaped region to represent “success”, in a vector space. |
| 171 | Uncovering Pattern Formation of Information Flow | Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu, Fei Wang | In this paper, by exploring 432 million information flow patterns extracted from a large-scale online social media dataset, we uncover a wide range of complex geometric patterns characterized by a three-dimensional metric space. |
| 172 | Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning | Yunchao Zhang, Yanjie Fu, Pengyang Wang, Xiaolin Li, Yu Zheng | Along these lines, we develop an unsupervised Collective Graph-regularized dual-Adversarial Learning (CGAL) framework for multi-view graph representation learning and also a Graph-regularized dual-Adversarial Learning (GAL) framework for single-view graph representation learning. |
| 173 | Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts | Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, Wei Wang | In this paper, we propose a novel two-view KG embedding model, JOIE, with the goal to produce better knowledge embedding and enable new applications that rely on multi-view knowledge. |
| 174 | Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning | Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang | To tackle these challenges, we proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once. |
TABLE 2: Data Science Track
| Title | Authors | Highlight | |
|---|---|---|---|
| 1 | 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com | Lucas Bernardi, Themistoklis Mavridis, Pablo Estevez | Our main conclusion is that an iterative, hypothesis driven process, integrated with other disciplines was fundamental to build 150 successful products enabled by Machine Learning. |
| 2 | A Collaborative Learning Framework to Tag Refinement for Points of Interest | Jingbo Zhou, Shan Gou, Renjun Hu, Dongxiang Zhang, Jin Xu, Airong Jiang, Ying Li, Hui Xiong | In this paper, we study the POI tag refinement problem which aims to automatically fill in the missing tags as well as correct noisy tags for POIs. |
| 3 | A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry | Lei Chen, Xialiang Tong, Mingxuan Yuan, Jia Zeng, Lei Chen | In this paper, we solve a Multi-Level Bin Packing (MLBP) problem in the real make-to-order industry scenario. |
| 4 | A Deep Generative Approach to Search Extrapolation and Recommendation | Fred X. Han, Di Niu, Haolan Chen, Kunfeng Lai, Yancheng He, Yu Xu | In this work, we propose a deep generative approach to construct a related search query for recommendation in a word-by-word fashion, given either an input query or the title of a document. |
| 5 | A Deep Value-network Based Approach for Multi-Driver Order Dispatching | Xiaocheng Tang, Zhiwei (Tony) Qin, Fan Zhang, Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, Jieping Ye | In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi’s ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics. |
| 6 | A Generalized Framework for Population Based Training | Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta | We propose a general, black-box PBT framework that distributes many asynchronous “trials” (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller. |
| 7 | A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR | Prithwish Chakraborty, Faisal Farooq | In this paper, we present a robust end-to-end machine learning based SaaS system to perform analysis on a very large EHR dataset. |
| 8 | A Severity Score for Retinopathy of Prematurity | Peng Tian, Yuan Guo, Jayashree Kalpathy-Cramer, Susan Ostmo, John Peter Campbell, Michael F. Chiang, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis | We propose a means of producing a continuous severity score in an automated fashion, regressed from both (a) diagnostic class labels as well as (b) comparison outcomes. |
| 9 | A Unified Framework for Marketing Budget Allocation | Kui Zhao, Junhao Hua, Ling Yan, Qi Zhang, Huan Xu, Cheng Yang | In this paper, we present a novel unified framework for marketing budget allocation. |
| 10 | A User-Centered Concept Mining System for Query and Document Understanding at Tencent | Bang Liu, Weidong Guo, Di Niu, Chaoyue Wang, Shunnan Xu, Jinghong Lin, Kunfeng Lai, Yu Xu | In this paper, we describe our experience of implementing and deploying ConcepT in Tencent QQ Browser. |
| 11 | AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 | Zhipeng Luo, Jianqiang Huang, Ke Hu, Xue Li, Peng Zhang | In this paper, we present AccuAir, our winning solution to the KDD Cup 2018 of Fresh Air, where the proposed solution has won the 1st place in two tracks, and the 2nd place in the other one. |
| 12 | Actions Speak Louder than Goals: Valuing Player Actions in Soccer | Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis | This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. |
| 13 | Active Deep Learning for Activity Recognition with Context Aware Annotator Selection | H M Sajjad Hossain, Nirmalya Roy | In this paper, we first propose an active learning combined deep model which updates its network parameters based on the optimization of a joint loss function. We then propose a novel annotator selection model by exploiting the relationships among the users while considering their heterogeneity with respect to their expertise, physical and spatial context.. |
| 14 | Adversarial Matching of Dark Net Market Vendor Accounts | Xiao Hui Tai, Kyle Soska, Nicolas Christin | By leveraging eight years of data, we investigate one such adversarial context: matching different online anonymous marketplace vendor handles to unique sellers. |
| 15 | AiAds: Automated and Intelligent Advertising System for Sponsored Search | Xiao Yang, Daren Sun, Ruiwei Zhu, Tao Deng, Zhi Guo, Zongyao Ding, Shouke Qin, Yanfeng Zhu | In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. |
| 16 | AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation | Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song | In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. |
| 17 | AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks | Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong | In this work, we propose AlphaStock, a novel reinforcement learning (RL) based investment strategy enhanced by interpretable deep attention networks, to address the above challenges. |
| 18 | Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning | Yichen Shen, Maxime Voisin, Alireza Aliamiri, Anand Avati, Awni Hannun, Andrew Ng | We develop an algorithm that accurately detects Atrial Fibrillation (AF) episodes from photoplethysmograms (PPG) recorded in ambulatory free-living conditions. |
| 19 | Anomaly Detection for an E-commerce Pricing System | Jagdish Ramakrishnan, Elham Shaabani, Chao Li, Matyas A. Sustik | In this paper, we describe unsupervised and supervised anomaly detection approaches we developed and deployed for a large-scale online pricing system at Walmart. |
| 20 | Applying Deep Learning to Airbnb Search | Malay Haldar, Mustafa Abdool, Prashant Ramanathan, Tao Xu, Shulin Yang, Huizhong Duan, Qing Zhang, Nick Barrow-Williams, Bradley C. Turnbull, Brendan M. Collins, Thomas Legrand | This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. |
| 21 | AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications | Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang | In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. |
| 22 | Auto-Keras: An Efficient Neural Architecture Search System | Haifeng Jin, Qingquan Song, Xia Hu | In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search. |
| 23 | Automatic Dialogue Summary Generation for Customer Service | Chunyi Liu, Peng Wang, Jiang Xu, Zang Li, Jieping Ye | In this paper, we introduce auxiliary key point sequences to solve this problem. |
| 24 | Bid Optimization by Multivariable Control in Display Advertising | Xun Yang, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu, Kun Gai | In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. |
| 25 | Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration | Lisa Singh, Laila Wahedi, Yanchen Wang, Yifang Wei, Christo Kirov, Susan Martin, Katharine Donato, Yaguang Liu, Kornraphop Kawintiranon | This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. |
| 26 | Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior | Long Guo, Lifeng Hua, Rongfei Jia, Binqiang Zhao, Xiaobo Wang, Bin Cui | In this paper, we propose a novel end-to-end deep network, named Deep Intent Prediction Network (DIPN), to predict real-time user purchasing intent. |
| 27 | Carousel Ads Optimization in Yahoo Gemini Native | Michal Aharon, Oren Somekh, Avi Shahar, Assaf Singer, Baruch Trayvas, Hadas Vogel, Dobri Dobrev | In this work we present a post-auction successive elimination based approach for ranking assets according to their click trough rate (CTR) and render the carousel accordingly, placing higher CTR assets in more conspicuous slots. |
| 28 | Chainer: A Deep Learning Framework for Accelerating the Research Cycle | Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent | In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. |
| 29 | Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study | Zenghua Xia, Chang Liu, Neil Zhenqiang Gong, Qi Li, Yong Cui, Dawn Song | In this work, we study a new type of OSN, called privacy-centric mobile social network (PC-MSN), such as KakaoTalk and LINE, which has attracted billions of users recently. |
| 30 | Characterizing and Forecasting User Engagement with In-App Action Graph: A Case Study of Snapchat | Yozen Liu, Xiaolin Shi, Lucas Pierce, Xiang Ren | In this paper, we answer the question whether users’ in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? |
| 31 | Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search | Pan Li, Zhen Qin, Xuanhui Wang, Donald Metzler | In this paper, we study how to combine DTs and NNs to effectively bring the benefits from both sides in the learning-to-rank setting. |
| 32 | Community Detection on Large Complex Attribute Network | Chen Zhe, Aixin Sun, Xiaokui Xiao | In this paper, we propose a framework named AGGMMR to effectively address the challenges come from scalability, mixed attributes, and incomplete value. |
| 33 | Constructing High Precision Knowledge Bases with Subjective and Factual Attributes | Ari Kobren, Pablo Barrio, Oksana Yakhnenko, Johann Hibschman, Ian Langmore | In this work, we develop a method for constructing KBs with tunable precision–i.e., KBs that can be made to operate at a specific false positive rate, despite storing both difficult-to-evaluate subjective attributes and more traditional factual attributes. |
| 34 | Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy | Jan-Philipp Schulze, Artur Mrowca, Elizabeth Ren, Hans-Andrea Loeliger, Konstantin B?ttinger | We propose a novel unsupervised approach that combines tools from deep learning and signal processing, working in a purely data-driven way. |
| 35 | Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives | Shunsuke Kitada, Hitoshi Iyatomi, Yoshifumi Seki | In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. |
| 36 | Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction | Wentao Ouyang, Xiuwu Zhang, Li Li, Heng Zou, Xin Xing, Zhaojie Liu, Yanlong Du | In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target ad. |
| 37 | Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting | Bin Wang, Jie Lu, Zheng Yan, Huaishao Luo, Tianrui Li, Yu Zheng, Guangquan Zhang | In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. |
| 38 | DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data | Anthony Sicilia, Konstantinos Pelechrinis, Kirk Goldsberry | In this study, we develop a deep learning framework DeepHoops to process a unique dataset composed of spatio-temporal tracking data from NBA games in order to generate a running stream of predictions on the expected points to be scored as a possession progresses. |
| 39 | DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery | Stephen Lee, Srinivasan Iyengar, Menghong Feng, Prashant Shenoy, Subhransu Maji | In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. |
| 40 | DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events | Renhe Jiang, Xuan Song, Dou Huang, Xiaoya Song, Tianqi Xia, Zekun Cai, Zhaonan Wang, Kyoung-Sook Kim, Ryosuke Shibasaki | Therefore in this study, we aim to extract the deep trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. |
| 41 | Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA | Shahroz Tariq, Sangyup Lee, Youjin Shin, Myeong Shin Lee, Okchul Jung, Daewon Chung, Simon S. Woo | In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). |
| 42 | Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition | Anil R. Yelundur, Vineet Chaoji, Bamdev Mishra | In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. |
| 43 | Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams | Richard Chen, Filip Jankovic, Nikki Marinsek, Luca Foschini, Lampros Kourtis, Alessio Signorini, Melissa Pugh, Jie Shen, Roy Yaari, Vera Maljkovic, Marc Sunga, Han Hee Song, Hyun Joon Jung, Belle Tseng, Andrew Trister | In this work, we present a platform for remote and unobtrusive monitoring of symptoms related to cognitive impairment using several consumer-grade smart devices. |
| 44 | Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners | Aleksander Fabijan, Jayant Gupchup, Somit Gupta, Jeff Omhover, Wen Qin, Lukas Vermeer, Pavel Dmitriev | The goal of this paper is to make diagnosing, fixing, and preventing SRMs easier. |
| 45 | DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview | Chuan Qin, Hengshu Zhu, Chen Zhu, Tong Xu, Fuzhen Zhuang, Chao Ma, Jingshuai Zhang, Hui Xiong | To this end, in this research, we focus on the development of a personalized question recommender system, namely DuerQuiz, for enhancing the job interview assessment. |
| 46 | Dynamic Pricing for Airline Ancillaries with Customer Context | Naman Shukla, Arinbj?rn Kolbeinsson, Ken Otwell, Lavanya Marla, Kartik Yellepeddi | This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers. |
| 47 | E.T.-RNN: Applying Deep Learning to Credit Loan Applications | Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin, Dmitrii Umerenkov | In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. |
| 48 | Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft | Kiri L. Wagstaff, Gary Doran, Ashley Davies, Saadat Anwar, Srija Chakraborty, Marissa Cameron, Ingrid Daubar, Cynthia Phillips | We describe algorithms that we designed to assist in three specific scientific investigations to be conducted during flybys of Jupiter’s moon Europa: the detection of thermal anomalies, compositional anomalies, and plumes of icy matter from Europa’s subsurface ocean. |
| 49 | Estimating Cellular Goals from High-Dimensional Biological Data | Laurence Yang, Michael A. Saunders, Jean-Christophe Lachance, Bernhard O. Palsson, Jos? Bento | Here, we develop the first approach to estimating constraint reactions from data that can scale to realistically large metabolic models. |
| 50 | Fairness in Recommendation Ranking through Pairwise Comparisons | Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow | In this paper we offer a set of novel metrics for evaluating algorithmic fairness concerns in recommender systems. |
| 51 | Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search | Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi | We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. |
| 52 | FDML: A Collaborative Machine Learning Framework for Distributed Features | Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou | We propose an asynchronous stochastic gradient descent (SGD) algorithm for such a feature distributed machine learning (FDML) problem, to jointly learn from distributed features, with theoretical convergence guarantees under bounded asynchrony. |
| 53 | Feedback Shaping: A Modeling Approach to Nurture Content Creation | Ye Tu, Chun Lo, Yiping Yuan, Shaunak Chatterjee | In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators. |
| 54 | Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences | Stephanie deWet, Jiafan Ou | We will describe a two-stage embedding-based audience expansion model that is deployed in production at Pinterest. |
| 55 | FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging | Doyen Sahoo, Wang Hao, Shu Ke, Wu Xiongwei, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Steven C. H. Hoi | With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities. |
| 56 | Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning | J. Weston Hughes, Keng-hao Chang, Ruofei Zhang | We jointly train a model to minimize cross-entropy on an existing corpus of Landing Page/Text Ad pairs using typical sequence to sequence training techniques while also optimizing the expected click-through rate (CTR) as predicted by an existing oracle model using SCST. |
| 57 | Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression | Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi | In this paper, we propose a novel method to demonstrate the benefits provided by RT measurements. |
| 58 | Gmail Smart Compose: Real-Time Assisted Writing | Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, Yonghui Wu | In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. |
| 59 | Hard to Park?: Estimating Parking Difficulty at Scale | Neha Arora, James Cook, Ravi Kumar, Ivan Kuznetsov, Yechen Li, Huai-Jen Liang, Andrew Miller, Andrew Tomkins, Iveel Tsogsuren, Yi Wang | In this paper we consider the problem of estimating the difficulty of parking at a particular time and place; this problem is a critical sub-component for any system providing parking assistance to users. |
| 60 | How to Invest my Time: Lessons from Human-in-the-Loop Entity Extraction | Shanshan Zhang, Lihong He, Eduard Dragut, Slobodan Vucetic | To get an answer, we consider an iterative human-in-the-loop (HIL) framework that allows users to write a regex or manually label entity mentions, followed by training and refining a classifier based on the provided information. |
| 61 | Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System | Hao Liu, Yongxin Tong, Panpan Zhang, Xinjiang Lu, Jianguo Duan, Hui Xiong | In this work, we propose Hydra, a recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). |
| 62 | Improving Subseasonal Forecasting in the Western U.S. with Machine Learning | Jessica Hwang, Paulo Orenstein, Judah Cohen, Karl Pfeiffer, Lester Mackey | Here we present and evaluate our machine learning approach to the Rodeo and release our SubseasonalRodeo dataset, collected to train and evaluate our forecasting system. |
| 63 | Infer Implicit Contexts in Real-time Online-to-Offline Recommendation | Xichen Ding, Jie Tang, Tracy Liu, Cheng Xu, Yaping Zhang, Feng Shi, Qixia Jiang, Dan Shen | In this paper, we propose a new approach, called Mixture Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit contexts and consequently, to improve the quality of real-time O2O recommendation. |
| 64 | IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation | Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, Xiaofei He | In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks. |
| 65 | Internal Promotion Optimization | Rupesh Gupta, Guangde Chen, Shipeng Yu | In this paper, we discuss our approach for optimization of internal promotions at LinkedIn. |
| 66 | Investigate Transitions into Drug Addiction through Text Mining of Reddit Data | John Lu, Sumati Sridhar, Ritika Pandey, Mohammad Al Hasan, Georege Mohler | In this work, we obtained data from Reddit, an online collection of forums, to gather insight into drug use/misuse using text snippets from users narratives. |
| 67 | Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction | Chi Chen, Li Zhao, Jiang Bian, Chunxiao Xing, Tie-Yan Liu | In this paper, we propose to extract and explore stock intrinsic properties to enhance stock trend prediction. |
| 68 | IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery | Dipendra Jha, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao, Alok Choudhary, Ankit Agrawal | In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. |
| 69 | Large-Scale Training Framework for Video Annotation | Seong Jae Hwang, Joonseok Lee, Balakrishnan Varadarajan, Ariel Gordon, Zheng Xu, Apostol (Paul) Natsev | In this paper, we present a MapReduce-based training framework, which exploits both data parallelism and model parallelism to scale training of complex video models. |
| 70 | Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework | Xiaoyang Ma, Lan Zhang, Lan Xu, Zhicheng Liu, Ge Chen, Zhili Xiao, Yang Wang, Zhengtao Wu | To address this issue, in this work, we conduct a thorough analysis on large-scale user visits data and propose a novel deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time series forecasting. |
| 71 | Learning a Unified Embedding for Visual Search at Pinterest | Andrew Zhai, Hao-Yu Wu, Eric Tzeng, Dong Huk Park, Charles Rosenberg | In this work we describe a multi-task deep metric learning system to learn a single unified image embedding which can be used to power our multiple visual search products. |
| 72 | Learning Sleep Quality from Daily Logs | Sungkyu Park, Cheng-Te Li, Sungwon Han, Cheng Hsu, Sang Won Lee, Meeyoung Cha | This study presents a computational framework for predicting sleep efficiency of insomnia sufferers. |
| 73 | Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data | Jackson A. Killian, Bryan Wilder, Amit Sharma, Vinod Choudhary, Bistra Dilkina, Milind Tambe | We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year.The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. |
| 74 | LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction | Yangli-ao Geng, Qingyong Li, Tianyang Lin, Lei Jiang, Liangtao Xu, Dong Zheng, Wen Yao, Weitao Lyu, Yijun Zhang | In this work, we propose a data-driven model based on neural networks, referred to as LightNet, for lightning prediction. |
| 75 | Machine Learning at Microsoft with ML.NET | Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu | In this paper we present ML.NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. |
| 76 | Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning | Megha Srivastava, Hoda Heidari, Andreas Krause | We take a descriptive approach and set out to identify the notion of fairness that best captures lay people’s perception of fairness. |
| 77 | MediaRank: Computational Ranking of Online News Sources | Junting Ye, Steven Skiena | In this work, we design and build MediaRank (urlwww.media-rank.com ), a fully automated system to rank over 50,000 online news sources around the world. |
| 78 | Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation | Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, Yongliang Li | In this paper, we propose to model the complex objects and rich interactions in intent recommendation as a Heterogeneous Information Network. |
| 79 | MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records | Xi Sheryl Zhang, Fengyi Tang, Hiroko H. Dodge, Jiayu Zhou, Fei Wang | In this paper, we propose MetaPred, a meta-learning framework for clinical risk prediction from longitudinal patient EHR. |
| 80 | MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu’s Sponsored Search | Miao Fan, Jiacheng Guo, Shuai Zhu, Shuo Miao, Mingming Sun, Ping Li | Specifically, this paper will elaborate on how we adopt active learning to overcome the insufficiency of click history at the matching layer when training our neural click networks offline, and how we use the SOTA ANN search technique for retrieving ads more efficiently (Here “ANN” stands for approximate nearest neighbor search). |
| 81 | MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data | Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey Kirshner | In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. |
| 82 | Multi-Horizon Time Series Forecasting with Temporal Attention Learning | Chenyou Fan, Yuze Zhang, Yi Pan, Xiaoyue Li, Chi Zhang, Rong Yuan, Di Wu, Wensheng Wang, Jian Pei, Heng Huang | We propose a novel data-driven approach for solving multi-horizon probabilistic forecasting tasks that predicts the full distribution of a time series on future horizons. |
| 83 | MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games | Jianrong Tao, Jianshi Lin, Shize Zhang, Sha Zhao, Runze Wu, Changjie Fan, Peng Cui | We present a multi-graph attention network (MGAT) in the graph structure view, a behavior attention network (BAN) in the vertex content view, a portrait attention network (PAN) in the vertex attribute view and a data source attention network (DSAN) in the data source view. |
| 84 | Naranjo Question Answering using End-to-End Multi-task Learning Model | Bhanu Pratap Singh Rawat, Fei Li, Hong Yu | In this study, we present the first attempt to automatically infer the causality between a drug and an ADR from electronic health records (EHRs) by answering the Naranjo questionnaire, the validated clinical question answering set used by domain experts for ADR causality assessment. |
| 85 | Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data — An Application to Climate Prediction | Yumin Liu, Junxiang Chen, Auroop Ganguly, Jennifer Dy | Motivated by the need of identifying which GCMs are more useful for a particular region and time, we introduce a clustering model combining Dirichlet Process (DP) mixture of sparse linear regression with Markov Random Fields (MRFs). |
| 86 | Nostalgin: Extracting 3D City Models from Historical Image Data | Amol Kapoor, Hunter Larco, Raimondas Kiveris | In this work, we describe Nostalgin (Nostalgia Engine), a method that can faithfully reconstruct cities from historical images. |
| 87 | NPA: Neural News Recommendation with Personalized Attention | Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie | In this paper, we propose a neural news recommendation model with personalized attention (NPA). |
| 88 | OAG: Toward Linking Large-scale Heterogeneous Entity Graphs | Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang | Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework — LinKG — to address the problem of building a large-scale linked entity graph. |
| 89 | OCC: A Smart Reply System for Efficient In-App Communications | Yue Weng, Huaixiu Zheng, Franziska Bell, Gokhan Tur | In this paper, we introduce Uber’s smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. |
| 90 | Online Amnestic DTW to allow Real-Time Golden Batch Monitoring | Chin-Chia Michael Yeh, Yan Zhu, Hoang Anh Dau, Amirali Darvishzadeh, Mikhail Noskov, Eamonn Keogh | In this work, we make two contributions to golden batch processing. |
| 91 | Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics | Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, Nitesh V. Chawla | To address these factors, we develop a Graph Multi-Scale Pyramid Networks (GMP) framework to fully exploit users’ latent behavioral patterns with both multi-scale temporal dynamics and arbitrary inter-dependencies among product categories. |
| 92 | Optuna: A Next-generation Hyperparameter Optimization Framework | Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama | The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. |
| 93 | Personalized Attraction Enhanced Sponsored Search with Multi-task Learning | Wei Zhao, Boxuan Zhang, Beidou Wang, Ziyu Guan, Wanxian Guan, Guang Qiu, Wei Ning, Jiming Chen, Hongmin Liu | We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. |
| 94 | Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching | Mathias Kraus, Stefan Feuerriegel | Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching |
| 95 | PinText: A Multitask Text Embedding System in Pinterest | Jinfeng Zhuang, Yu Liu | In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. |
| 96 | POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion | Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao | In this paper, we demonstrate these two requirements can be satisfied via building a bridge between outfit generation and recommendation. |
| 97 | Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction | Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai | In this paper, we face directly the challenge of long sequential user behavior modeling and introduce our hands-on practice with the co-design of machine learning algorithm and online serving system for CTR prediction task. |
| 98 | Precipitation Nowcasting with Satellite Imagery | Vadim Lebedev, Vladimir Ivashkin, Irina Rudenko, Alexander Ganshin, Alexander Molchanov, Sergey Ovcharenko, Ruslan Grokhovetskiy, Ivan Bushmarinov, Dmitry Solomentsev | We have developed a method for precipitation nowcasting based on geostationary satellite imagery and incorporated the resulting data into the Yandex.Weather precipitation map (including an alerting service with push notifications for products in the Yandex ecosystem), thus expanding its coverage and paving the way to a truly global nowcasting service. |
| 99 | Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising | Junwei Pan, Yizhi Mao, Alfonso Lobos Ruiz, Yu Sun, Aaron Flores | In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. |
| 100 | Predicting Economic Development using Geolocated Wikipedia Articles | Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon | Here we propose a novel method for estimating socioeconomic indicators using open-source, geolocated textual information from Wikipedia articles. |
| 101 | Predicting Evacuation Decisions using Representations of Individuals’ Pre-Disaster Web Search Behavior | Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Satish V. Ukkusuri | In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. |
| 102 | Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being | Ehimwenma Nosakhare, Rosalind Picard | In this paper, we present a framework to 1) map multi-modal messy data collected in the “wild” to meaningful feature representations of health behavior, and 2) uncover latent patterns comprising multiple health behaviors that best predict well-being. |
| 103 | Pythia: AI-assisted Code Completion System | Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, Neel Sundaresan | In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. |
| 104 | Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches | Shiwen Zhao, Brandt Westing, Shawn Scully, Heri Nieto, Roman Holenstein, Minwoo Jeong, Krishna Sridhar, Brandon Newendorp, Mike Bastian, Sethu Raman, Tim Paek, Kevin Lynch, Carlos Guestrin | This paper describes a new way to invoke IVAs on smartwatches: simply raise your hand and speak naturally. |
| 105 | Randomized Experimental Design via Geographic Clustering | David Rolnick, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab Mirrokni, Amir Najmi | Our main technical contribution is a statistical framework to measure the effectiveness of clusterings. |
| 106 | Ranking in Genealogy: Search Results Fusion at Ancestry | Peng Jiang, Yingrui Yang, Gann Bierner, Fengjie Alex Li, Ruhan Wang, Azadeh Moghtaderi | Herein, we provide an overview of our solutions to overcome such record disparity problems in the Ancestry search engine. |
| 107 | Real-time Attention Based Look-alike Model for Recommender System | Yudan Liu, Kaikai Ge, Xu Zhang, Leyu Lin | This paper introduces a real-time attention based look-alike model (RALM) for recommender systems, which tackles the challenge of conflict between real-time and effectiveness. |
| 108 | Real-time Event Detection on Social Data Streams | Mateusz Fedoryszak, Brent Frederick, Vijay Rajaram, Changtao Zhong | We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. |
| 109 | Real-time On-Device Troubleshooting Recommendation for Smartphones | Keiichi Ochiai, Kohei Senkawa, Naoki Yamamoto, Yuya Tanaka, Yusuke Fukazawa | Here, we design and implement a system that based on the user’s smartphone activity detects that the user has a problem and requires help. |
| 110 | Real-World Product Deployment of Adaptive Push Notification Scheduling on Smartphones | Tadashi Okoshi, Kota Tsubouchi, Hideyuki Tokuda | In this paper, we construct a new interruptibility estimation and adaptive notification scheduling with redesigned technical components. |
| 111 | Recurrent Neural Networks for Stochastic Control in Real-Time Bidding | Nicolas Grislain, Nicolas Perrin, Antoine Thabault | Recurrent Neural Networks for Stochastic Control in Real-Time Bidding |
| 112 | Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems | Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, Dawei Yin | To address these issues, in this work, we introduce a RL framework — FeedRec to optimize the long-term user engagement. |
| 113 | Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising | Achir Kalra, Chong Wang, Cristian Borcea, Yi Chen | In this paper, we study the problem of estimating the failure rate of a reserve price, i.e., the probability that a reserve price fails to be outbid. |
| 114 | Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network | Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei | This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. |
| 115 | Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement | Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin | In this work, we consider a data-mining approach to enhance the GPS signal. |
| 116 | Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points | Zhibin Li, Jian Zhang, Qiang Wu, Yongshun Gong, Jinfeng Yi, Christina Kirsch | In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. |
| 117 | Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs | Thien Q. Tran, Jun Sakuma | In this work, we proposed a novel feature selection method to overcome this instability problem. |
| 118 | Seeker: Real-Time Interactive Search | Ari Biswas, Thai T. Pham, Michael Vogelsong, Benjamin Snyder, Houssam Nassif | This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes. |
| 119 | Semantic Product Search | Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian (Allen) Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, Bing Yin | We study the problem of semantic matching in product search, that is, given a customer query, retrieve all semantically related products from the catalog. |
| 120 | Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data | Dimitris Spathis, Sandra Servia-Rodriguez, Katayoun Farrahi, Cecilia Mascolo, Jason Rentfrow | In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. |
| 121 | Sequential Scenario-Specific Meta Learner for Online Recommendation | Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang | This paper addresses such problems usingfew-shot learning andmeta learning. |
| 122 | Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data | Sobhan Moosavi, Mohammad Hossein Samavatian, Arnab Nandi, Srinivasan Parthasarathy, Rajiv Ramnath | We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation patterns and the other to explore influential patterns. |
| 123 | Shrinkage Estimators in Online Experiments | Drew Dimmery, Eytan Bakshy, Jasjeet Sekhon | In this work we develop consistent, small bias, shrinkage estimators for this setting. |
| 124 | Smart Roles: Inferring Professional Roles in Email Networks | Di Jin, Mark Heimann, Tara Safavi, Mengdi Wang, Wei Lee, Lindsay Snider, Danai Koutra | Toward our goal, in this paper we study professional role inference on a unique new email dataset comprising billions of email exchanges across thousands of organizations. |
| 125 | SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine | Abhilash Reddy Chenreddy, Parshan Pakiman, Selvaprabu Nadarajah, Ranganathan Chandrasekaran, Rick Abens | We propose an SM Optimization and Inverse Learning Engine (SMOILE) that combines optimization and inverse reinforcement learning to streamline implementation. |
| 126 | Social Skill Validation at LinkedIn | Xiao Yan, Jaewon Yang, Mikhail Obukhov, Lin Zhu, Joey Bai, Shiqi Wu, Qi He | In this paper, we develop the Social Skill Validation, a novel framework of collecting validations for members’ skill expertise at the scale of billions of member-skill pairs. |
| 127 | Structured Noise Detection: Application on Well Test Pressure Derivative Data | Farhan Asif Chowdhury, Satomi Suzuki, Abdullah Mueen | In this paper, we use the Singular Spectrum Analysis (SSA) to decompose PTA data into additive components; subsequently we use the eigenvalues associated with the decomposed components to identify the components that contain most of the structured noise information. |
| 128 | Temporal Probabilistic Profiles for Sepsis Prediction in the ICU | Eitam Sheetrit, Nir Nissim, Denis Klimov, Yuval Shahar | Here, we propose a new dynamic-behavior-based model, which we call a Temporal Probabilistic proFile (TPF), for classification and prediction tasks of multivariate time series. |
| 129 | TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank | Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, Stephan Wolf | We introduce TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. |
| 130 | The Error is the Feature: How to Forecast Lightning using a Model Prediction Error | Christian Sch?n, Jens Dittrich, Richard M?ller | We therefore present a new approach to the problem of predicting thunderstorms based on machine learning. |
| 131 | The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis | Xuan Yin, Liangjie Hong | In this paper, we introduce causal mediation analysis as a formal statistical tool to reveal the underlying causal mechanisms. |
| 132 | The Secret Lives of Names?: Name Embeddings from Social Media | Junting Ye, Steven Skiena | In this paper, we explore learning name embeddings from public Twitter data. |
| 133 | Time-Series Anomaly Detection Service at Microsoft | Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang | In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. |
| 134 | Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation | Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao | In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. |
| 135 | Towards Identifying Impacted Users in Cellular Services | Shobha Venkataraman, Jia Wang | In this paper, we present LOTUS, a system that identifies users impacted by a common root cause (such as a network outage) from user feedback. |
| 136 | Towards Knowledge-Based Personalized Product Description Generation in E-commerce | Qibin Chen, Junyang Lin, Yichang Zhang, Hongxia Yang, Jingren Zhou, Jie Tang | In this paper, we explore a new way to generate personalized product descriptions by combining the power of neural networks and knowledge base. |
| 137 | Towards Sustainable Dairy Management – A Machine Learning Enhanced Method for Estrus Detection | Kevin Fauvel, V?ronique Masson, ?lisa Fromont, Philippe Faverdin, Alexandre Termier | Our research tackles the challenge of milk production resource use efficiency in dairy farms with machine learning methods. |
| 138 | TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme | Zheyi Pan, Jie Bao, Weinan Zhang, Yong Yu, Yu Zheng | To this end, we propose a novel trajectory copyright protection scheme, which can protect trajectory data from comprehensive types of data modifications/attacks. |
| 139 | TV Advertisement Scheduling by Learning Expert Intentions | Yasuhisa Suzuki, Wemer M. Wee, Itaru Nishioka | This paper considers the automation of a typical complex advertisement scheduling system in broadcast television (TV) networks. |
| 140 | Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform | Tom S?hr, Asia J. Biega, Meike Zehlike, Krishna P. Gummadi, Abhijnan Chakraborty | In this paper, we analyze job assignments of a major taxi company and observe that there is significant inequality in the driver income distribution. |
| 141 | Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena | Yunfei Lu, Linyun Yu, Peng Cui, Chengxi Zang, Renzhe Xu, Yihao Liu, Lei Li, Wenwu Zhu | As a result, we propose a novel survival model, which incorporates both social and content factors, to predict churn probability over time. |
| 142 | Understanding Consumer Journey using Attention based Recurrent Neural Networks | Yichao Zhou, Shaunak Mishra, Jelena Gligorijevic, Tarun Bhatia, Narayan Bhamidipati | To address challenges in the two tasks, we propose an attention based recurrent neural network (RNN) which ingests a user activity trail, and predicts the user’s conversion probability along with attention weights for each activity (analogous to its position in the funnel). |
| 143 | Understanding the Role of Style in E-commerce Shopping | Hao Jiang, Aakash Sabharwal, Adam Henderson, Diane Hu, Liangjie Hong | In this paper, we discuss a novel process by which we leverage 43 named styles given by merchandising experts in order to bootstrap large-scale style prediction and analysis of how style impacts purchase decision. |
| 144 | Unsupervised Clinical Language Translation | Wei-Hung Weng, Yu-An Chung, Peter Szolovits | We show that a framework using representation learning, bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation, and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability, respectively, on sentence translation. |
| 145 | UrbanFM: Inferring Fine-Grained Urban Flows | Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng | In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. |
| 146 | Using Twitter to Predict When Vulnerabilities will be Exploited | Haipeng Chen, Rui Liu, Noseong Park, V.S. Subrahmanian | In this paper, we propose a novel framework to predict when a vulnerability will be exploited via Twitter discussion, without using CVSS score information. |
| 147 | Whole Page Optimization with Global Constraints | Weicong Ding, Dinesh Govindaraj, S V N Vishwanathan | We present the first unified framework for dealing with relevance, diversity, and business constraints simultaneously. |