Paper Digest: KDD 2017 Highlights

August 1, 2017June 25, 2020 admin

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is one of the top data mining conferences in the world.

To help the 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 / summaries to quickly get the main idea of each paper.

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TABLE 1: KDD 2017 Papers

	Title	Authors	Highlight
1	What’s Fair?	Cynthia Dwork	The talk will discuss the nascent mathematically rigorous study of fairness in classification and scoring.
2	The Future of Data Integration	Renée J. Miller	In this talk, I present some important innovations in data integration over the last two decades.
3	Three Principles of Data Science: Predictability, Stability and Computability	Bin Yu	In this talk, I’d like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions.
4	Foreword to the Applied Data Science: Invited Talks Track at KDD-2017	Usama M. Fayyad, Evangelos Simoudis, Ashok Srivastava	The primary focus on KDD is on peer-reviewed research contributions and the academic advancement of the field.
5	More than the Sum of its Parts: Building Domino Data Lab	Eduardo Ariño de la Rubia	In this talk, I will discuss how and why we built the Domino Data Lab platform.
6	Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy	Andy Berglund	In this talk I will describe how genome sequencing has dramatically improved our understanding of the most common adult form of muscular dystrophy, which is myotonic dystrophy.
7	Industrial Machine Learning	Josh Bloom	Particularly, I focus on three key areas: combining physics-based models to data-driven models, differential privacy and secure ML (including edge-to-cloud strategies), and interpretability of model predictions.
8	Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management	Longbing Cao	This talk introduces some of real-life applications of behavior informatics in core business, capital markets and government services.
9	It Takes More than Math and Engineering to Hit the Bullseye with Data	Paritosh Desai	This talk will explore effective practices and processes — the do’s and don’ts — for data scientists to succeed in large, complex organizations like a retailer with 1,800+ stores, major marketing campaigns across multiple channels and a fast growing online business.
10	Planning and Learning under Uncertainty: Theory and Practice	Jonathan P. How	This talk will describe recent progress on modeling, planning, learning, and control of autonomous systems operating in dynamic environments, with an emphasis on addressing the challenges faced on various timescales.
11	Big Data in Climate: Opportunities and Challenges for Machine Learning	Anuj Karpatne, Vipin Kumar	We discuss the challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change.
12	Addressing Challenges with Big Data for Media Measurement	Mainak Mazumdar	In this paper, we will demonstrate how Nielsen is combining proprietary ground truth data and methodologies with Big Data to address the accuracy and bias/variance challenges.
13	Machine Learning Software in Practice: Quo Vadis?	Szilárd Pafka	In this talk we will give a couple of examples of this mismatch.
14	Designing AI at Scale to Power Everyday Life	Rajesh Parekh	This talk will look at how Facebook is conducting and applying industry-leading research to help drive advancements in AI disciplines like computer vision, language understanding, speech and video.
15	Spaceborne Data Enters the Mainstream	David Potere	Along the way, we will explore some of the beautiful imagery of our home planet that fuels this new class of insights.
16	Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?	Usama M. Fayyad, Arno Candel, Eduardo Ariño de la Rubia, Szilárd Pafka, Anthony Chong, Jeong-Yoon Lee	Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?
17	The Future of Artificially Intelligent Assistants	Muthu Muthukrishnan, Andrew Tomkins, Larry Heck, Alborz Geramifard, Deepak Agarwal	In this panel, we will address the product and technology landscape, and will ask a series of experts in the field plus the members of the audience to take a stance on what the future of artificially intelligent assistants will look like.
18	Learning Certifiably Optimal Rule Lists	Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, Cynthia Rudin	We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
19	Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks	Chen Avin, Zvi Lotker, Yinon Nahum, David Peleg	This, in turn, enables us to estimate other important quantities, e.g., the size of the k-rich club, namely, the set of all nodes with a degree at least k. Finally, we introduce a new generalized model, G(p_t, r_t, q_t), which extends G(p) by allowing also time-varying probabilities for node and edge arrivals, as well as the formation of new components.
20	Unsupervised Network Discovery for Brain Imaging Data	Zilong Bai, Peter Walker, Anna Tschiffely, Fei Wang, Ian Davidson	Whereas previous work requires strong supervision, we propose an unsupervised matrix tri-factorization formulation with complex constraints and spatial regularization.
21	Patient Subtyping via Time-Aware LSTM Networks	Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou	In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention.
22	Robust Top-	Xiaojun Chang, Yao-Liang Yu, Yi Yang	Computationally, using the Jordan decomposition Lemma we show how to rewrite our objective as the difference of two convex functions, based on which we develop an efficient algorithm that allows incorporating many popular regularizers (such as the l2 and l1 norms).
23	KATE: K-Competitive Autoencoder for Text	Yu Chen, Mohammed J. Zaki	In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents.
24	A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection	Reuven Cohen, Liran Katzir, Aviv Yehezkel	In this paper we develop a new algorithm for this problem, based on the Maximum Likelihood (ML) method.
25	HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics	Edith Cohen	Our key insight is relating these target statistics to the complement Laplace transform of the input frequencies.
26	Fast Enumeration of Large k-Plexes	Alessio Conte, Donatella Firmani, Caterina Mordente, Maurizio Patrignani, Riccardo Torlone	In this paper we propose a new approach for enumerating large k-plexes in networks that speeds up the search by several orders of magnitude, leveraging on (i) methods for strongly reducing the search space and (ii) efficient techniques for the computation of maximal cliques.
27	Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery	Hoang Anh Dau, Eamonn Keogh	In this work, we explain the reasons behind these issues, and introduce a novel and general framework to address them.
28	metapath2vec: Scalable Representation Learning for Heterogeneous Networks	Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami	We study the problem of representation learning in heterogeneous networks.
29	Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters	Alessandro Epasto, Silvio Lattanzi, Renato Paes Leme	We propose ego-splitting, a new framework for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters.
30	Contextual Motifs: Increasing the Utility of Motifs using Contextual Data	Ian Fox, Lynn Ang, Mamta Jaiswal, Rodica Pop-Busui, Jenna Wiens	Thus, we propose an extension to motifs, contextual motifs, that incorporates context.
31	Unsupervised P2P Rental Recommendations via Integer Programming	Yanjie Fu, Guannan Liu, Mingfei Teng, Charu Aggarwal	We present an integer programming based recommender systems, where both accommodation benefits and community risks of lodging places are measured and incorporated into an objective function as utility measurements.
32	The Co-Evolution Model for Social Network Evolving and Opinion Migration	Yupeng Gu, Yizhou Sun, Jianxi Gao	In this paper, we propose a co-evolution model that closes the loop by modeling the two phenomena together, which contains two major components: (1) a network generative model when the node property is known; and (2) a property migration model when the social network structure is known.
33	Groups-Keeping Solution Path Algorithm for Sparse Regression with Automatic Feature Grouping	Bin Gu, Guodong Liu, Heng Huang	To address this challenge, in this paper, we propose a groups-keeping solution path algorithm to solve the OSCAR model (OscarGKPath).
34	Clustering Individual Transactional Data for Masses of Users	Riccardo Guidotti, Anna Monreale, Mirco Nanni, Fosca Giannotti, Dino Pedreschi	In this paper we focus on the problem of clustering individual transactional data for a large mass of users.
35	Network Inference via the Time-Varying Graphical Lasso	David Hallac, Youngsuk Park, Stephen Boyd, Jure Leskovec	In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data.
36	Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data	David Hallac, Sagar Vare, Stephen Boyd, Jure Leskovec	Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC).
37	Efficient Correlated Topic Modeling with Topic Embedding	Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing	In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors.
38	Accelerating Innovation Through Analogy Mining	Tom Hope, Joel Chan, Aniket Kittur, Dafna Shahaf	In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose.
39	Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines	Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon	In this paper, we show how to overcome the important challenge of speeding up kernel machines using multiple computers.
40	A Hierarchical Algorithm for Extreme Clustering	Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum	This paper introduces PERCH, a new non-greedy, incremental algorithm for hierarchical clustering that scales to both massive N and K—a problem setting we term extreme clustering.
41	Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing	Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang	In this paper, we propose a data-driven Differentiated Confounder Balancing (DCB) algorithm to jointly select confounders, differentiate weights of confounders and balance confounder distributions for treatment effect estimation in the wild high dimensional settings.
42	The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables	Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan	Here we propose a novel framework for evaluating the performance of predictive models on selectively labeled data.
43	Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics	Xiaoli Li, Jun Huan	In this paper we adopt a contemporary philosophical concept called "constructivism", which is a theory regarding how human learns.
44	Is the Whole Greater Than the Sum of Its Parts?	Liangyue Li, Hanghang Tong, Yong Wang, Conglei Shi, Nan Cao, Norbou Buchler	In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes.
45	Collaborative Variational Autoencoder for Recommender Systems	Xiaopeng Li, James She	This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario.
46	Linearized GMM Kernels and Normalized Random Fourier Features	Ping Li	Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate.
47	Discrete Content-aware Matrix Factorization	Defu Lian, Rui Liu, Yong Ge, Kai Zheng, Xing Xie, Longbing Cao	To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue.
48	Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams	Junming Liu, Yanjie Fu, Jingci Ming, Yong Ren, Leilei Sun, Hui Xiong	To this end, we develop an iterative analyzer for classifying encrypted mobile traffic in a real-time way.
49	Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning	Tingjin Luo, Weizhong Zhang, Shang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye, Jie Wang	In this paper, we propose a novel approach to differentiate the functions of PCIs by integrating sparse simplex projection—that is, a nonconvex sparsity-inducing regularizer—with the framework of multi-instance learning (MIL).
50	Discovering Reliable Approximate Functional Dependencies	Panagiotis Mandros, Mario Boley, Jilles Vreeken	These are exactly the questions we answer in this paper.
51	Towards an Optimal Subspace for K-Means	Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm	We propose SUBKMEANS, which extends the classic k-means algorithm.
52	SPARTan: Scalable PARAFAC2 for Large & Sparse Data	Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun	In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan.
53	struc2vec: Learning Node Representations from Structural Identity	Leonardo F.R. Ribeiro, Pedro H.P. Saverese, Daniel R. Figueiredo	This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes.
54	Similarity Forests	Saket Sathe, Charu C. Aggarwal	In this paper, we propose a method for extending random forests to work with any arbitrary set of data objects, as long as similarities can be computed among the data objects.
55	Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping	Parikshit Shah, Akshay Soni, Troy Chevalier	We describe an online algorithm for performing this optimization.
56	On Finding Socially Tenuous Groups for Online Social Networks	Chih-Ya Shen, Liang-Hao Huang, De-Nian Yang, Hong-Han Shuai, Wang-Chien Lee, Ming-Syan Chen	In this paper, we introduce the notion of k-triangles to measure the tenuity of a group.
57	PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks	Yu Shi, Po-Wei Chan, Honglei Zhuang, Huan Gui, Jiawei Han	Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective.
58	Multi-Aspect Streaming Tensor Completion	Qingquan Song, Xiao Huang, Hancheng Ge, James Caverlee, Xia Hu	To bridge this gap, we propose a Multi-Aspect Streaming Tensor completion framework (MAST) based on CANDECOMP/PARAFAC (CP) decomposition to track the subspace of general incremental tensors for completion.
59	Scalable and Sustainable Deep Learning via Randomized Hashing	Ryan Spring, Anshumali Shrivastava	We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks.
60	AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification	Yukihiro Tagami	In this paper, we present a novel graph embedding method called "AnnexML".
61	Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking	Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, Mounia Lalmas	In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones.
62	Structural Deep Brain Network Mining	Shen Wang, Lifang He, Bokai Cao, Chun-Ta Lu, Philip S. Yu, Ann B. Ragin	In this paper, we propose a Structural Deep Brain Network mining method, namely SDBN, to learn highly non-linear and structure-preserving representations of brain networks.
63	Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods	Suhang Wang, Charu Aggarwal, Huan Liu	Feature engineering has found increasing interest in recent years because of its ability to improve the effectiveness of various machine learning models.
64	Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes	Pengfei Wang, Yanjie Fu, Guannan Liu, Wenqing Hu, Charu Aggarwal	To that end, in this paper, we develop a joint model that integrates Mixture of Hawkes Process (MHP) with a hierarchical topic model to capture the arrival sequences with mixed trip purposes.
65	FORA: Simple and Effective Approximate Single-Source Personalized PageRank	Sibo Wang, Renchi Yang, Xiaokui Xiao, Zhewei Wei, Yin Yang	Motivated by this, we propose FORA, a simple and effective index-based solution for approximate SSPPR processing, with rigorous guarantees on result quality.
66	Large-scale Collaborative Ranking in Near-Linear Time	Liwei Wu, Cho-Jui Hsieh, James Sharpnack	In this paper, we consider the Collaborative Ranking (CR) problem for recommendation systems.
67	HoORaYs: High-order Optimization of Rating Distance for Recommender Systems	Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu	We formulate it as a regularized optimization problem, and propose an effective and scalable algorithm to solve it.
68	Collaboratively Improving Topic Discovery and Word Embeddings by Coordinating Global and Local Contexts	Guangxu Xun, Yaliang Li, Jing Gao, Aidong Zhang	In this paper, we propose a unified language model based on matrix factorization techniques which 1) takes the complementary global and local context information into consideration simultaneously, and 2) models topics and learns word embeddings collaboratively.
69	PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification	Ian E.H. Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit Dhillon, Eric Xing	In this work, we extend PD-Sparse to be efficiently parallelized in large-scale distributed settings.
70	Local Higher-Order Graph Clustering	Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich	Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph.
71	Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity	Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu	We propose three key ingredients, namely average-effect, multiscale-effect and correlation-effect, which govern the observed growth patterns at microscopic level.
72	Weisfeiler-Lehman Neural Machine for Link Prediction	Muhan Zhang, Yixin Chen	In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman Neural Machine (WLNM), which learns topological features in the form of graph patterns that promote the formation of links.
73	EmbedJoin: Efficient Edit Similarity Joins via Embeddings	Haoyu Zhang, Qin Zhang	In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold.
74	TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams	Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, Jiawei Han	We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection.
75	Graph Edge Partitioning via Neighborhood Heuristic	Chenzi Zhang, Fan Wei, Qin Liu, Zhihao Gavin Tang, Zhenguo Li	We consider the edge partitioning problem that partitions the edges of an input graph into multiple balanced components, while minimizing the total number of vertices replicated (one vertex might appear in more than one partition).
76	Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling	Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye	In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an m-by-n matrix in only O(m+n) time and space.
77	Tracking the Dynamics in Crowdfunding	Hongke Zhao, Hefu Zhang, Yong Ge, Qi Liu, Enhong Chen, Huayu Li, Le Wu	Crowdfunding is an emerging Internet fundraising mechanism by raising monetary contributions from the crowd for projects or ventures.
78	Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks	Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lun Lee	With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features.
79	Coresets for Kernel Regression	Yan Zheng, Jeff M. Phillips	In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error.
80	A Local Algorithm for Structure-Preserving Graph Cut	Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, Jingrui He	In this paper, we focus on mining user-specified high-order network structures and aim to find a structure-rich subgraph which does not break many such structures by separating the subgraph from the rest.
81	Anomaly Detection with Robust Deep Autoencoders	Chong Zhou, Randy C. Paffenroth	Such "Group Robust Deep Autoencoders (GRDA)" give rise to novel anomaly detection approaches whose superior performance we demonstrate on a selection of benchmark problems.
82	Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers	Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims	In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies.
83	Tripoles: A New Class of Relationships in Time Series Data	Saurabh Agrawal, Gowtham Atluri, Anuj Karpatne, William Haltom, Stefan Liess, Snigdhansu Chatterjee, Vipin Kumar	In this work, we define a novel relationship pattern involving three interacting time series, which we refer to as a tripole.
84	Post Processing Recommender Systems for Diversity	Arda Antikacioglu, R. Ravi	We address the problem of increasing diversity in recom- mendation systems that are based on collaborative filtering that use past ratings to predict a rating quality for potential recommendations.
85	Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews	Konstantin Bauman, Bing Liu, Alexander Tuzhilin	In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items.
86	Bolt: Accelerated Data Mining with Fast Vector Compression	Davis W. Blalock, John V. Guttag	We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x.
87	Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings	Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann	In this work, we propose a robust spectral clustering technique able to handle such scenarios.
88	DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection	Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan, Alex D. Leow	In this study, participants were provided a mobile phone to use as their primary phone.
89	Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization	Jinghui Chen, Quanquan Gu	We propose a fast Newton hard thresholding pursuit algorithm for sparsity constrained nonconvex optimization.
90	On Sampling Strategies for Neural Network-based Collaborative Filtering	Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong	In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.
91	Unsupervised Feature Selection in Signed Social Networks	Kewei Cheng, Jundong Li, Huan Liu	In this paper, we study a novel problem of unsupervised feature selection in signed social networks and propose a novel framework SignedFS.
92	GRAM: Graph-based Attention Model for Healthcare Representation Learning	Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun	To address these challenges, we propose GRaph-based Attention Model (GRAM) that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies.
93	Algorithmic Decision Making and the Cost of Fairness	Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, Aziz Huq	To mitigate such disparities, several techniques have recently been proposed to achieve algorithmic fairness.
94	Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks	Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla	Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks.
95	Revisiting Power-law Distributions in Spectra of Real World Networks	Nicole Eikmeier, David F. Gleich	By studying a large number of real world graphs, we find empirical evidence that most real world graphs have a statistically significant power-law distribution with a cutoff in the singular values of the adjacency matrix and eigenvalues of the Laplacian matrix in addition to the commonly conjectured power-law in the degrees.
96	REMIX: Automated Exploration for Interactive Outlier Detection	Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong	In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting.
97	Anarchists, Unite: Practical Entropy Approximation for Distributed Streams	Moshe Gabel, Daniel Keren, Assaf Schuster	We propose a practical communication-efficient algorithm for continuously approximating the entropy of distributed streams, with deterministic, user-defined error bounds.
98	Recurrent Poisson Factorization for Temporal Recommendation	Seyed Abbas Hosseini, Keivan Alizadeh, Ali Khodadadi, Ali Arabzadeh, Mehrdad Farajtabar, Hongyuan Zha, Hamid R. Rabiee	In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback.
99	SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis	Qiming Huang, Michael Zhu	We develop a novel method called SParse Optimal Transformations (SPOT) to simultaneously select important variables and explore relationships between the response and predictor variables in high dimensional nonparametric regression analysis.
100	Incremental Dual-memory LSTM in Land Cover Prediction	Xiaowei Jia, Ankush Khandelwal, Guruprasad Nayak, James Gerber, Kimberly Carlson, Paul West, Vipin Kumar	In this paper, we propose an LSTM-based spatio-temporal learning framework with a dual-memory structure.
101	MetaPAD: Meta Pattern Discovery from Massive Text Corpora	Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M. Kaplan, Timothy P. Hanratty, Jiawei Han	In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context.
102	Federated Tensor Factorization for Computational Phenotyping	Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang	In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
103	Statistical Emerging Pattern Mining with Multiple Testing Correction	Junpei Komiyama, Masakazu Ishihata, Hiroki Arimura, Takashi Nishibayashi, Shin-ichi Minato	We propose two emerging pattern mining methods: the first one controls FWER, and the second one controls FDR.
104	Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites	Igor Labutov, Yun Huang, Peter Brusilovsky, Daqing He	In this paper, we address one core, long-standing problem towards this goal: identifying outcome and prerequisite concepts within a piece of educational content (e.g., a tutorial).
105	Prospecting the Career Development of Talents: A Survival Analysis Perspective	Huayu Li, Yong Ge, Hengshu Zhu, Hui Xiong, Hongke Zhao	To this end, in this paper, we propose a novel survival analysis approach to model the talent career paths, with a focus on two critical issues in talent management, namely turnover and career progression.
106	A Context-aware Attention Network for Interactive Question Answering	Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav	To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available.
107	Distributed Multi-Task Relationship Learning	Sulin Liu, Sinno Jialin Pan, Qirong Ho	Therefore, in this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm.
108	Point-of-Interest Demand Modeling with Human Mobility Patterns	Yanchi Liu, Chuanren Liu, Xinjiang Lu, Mingfei Teng, Hengshu Zhu, Hui Xiong	To this end, in this paper, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data.
109	Functional Zone Based Hierarchical Demand Prediction For Bike System Expansion	Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, Hui Xiong	To address these challenges, in this paper, we develop a hierarchical station bike demand predictor which analyzes bike demands from functional zone level to station level.
110	Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources	Fenglong Ma, Chuishi Meng, Houping Xiao, Qi Li, Jing Gao, Lu Su, Aidong Zhang	In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects.
111	Let’s See Your Digits: Anomalous-State Detection using Benford’s Law	Samuel Maurus, Claudia Plant	In this paper we begin by showing that system metrics generated by many modern information systems like Twitter, Wikipedia, YouTube and GitHub obey this law.
112	Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting	Guo-Jun Qi, Jiliang Tang, Jingdong Wang, Jiebo Luo	We conduct experiments on three forecasting problems, covering sensor and market data streams.
113	Automatic Synonym Discovery with Knowledge Bases	Meng Qu, Xiang Ren, Jiawei Han	In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus.
114	An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance	Edward Raff, Charles Nicholas	We introduce an alternative metric also inspired by compression, the Lempel-Ziv Jaccard Distance (LZJD).
115	Inferring the Strength of Social Ties: A Community-Driven Approach	Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis	In this paper we study the problem of inferring the strength of social ties in a given network.
116	Detecting Network Effects: Randomizing Over Randomized Experiments	Martin Saveski, Jean Pouget-Abadie, Guillaume Saint-Jacques, Weitao Duan, Souvik Ghosh, Ya Xu, Edoardo M. Airoldi	In this paper, we leverage a new experimental design for testing whether SUTVA holds, without making any assumptions on how treatment effects may spill over between the treatment and the control group.
117	When is a Network a Network?: Multi-Order Graphical Model Selection in Pathways and Temporal Networks	Ingo Scholtes	We introduce a framework for the modeling of sequential data capturing pathways of varying lengths observed in a network.
118	ReasoNet: Learning to Stop Reading in Machine Comprehension	Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen	In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks.
119	DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams	Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos	We propose DENSESTREAM, an incremental algorithm that maintains and updates a dense subtensor in a tensor stream (i.e., a sequence of changes in a tensor), and DENSESALERT, an incremental algorithm spotting the sudden appearances of dense subtensors.
120	Anomaly Detection in Streams with Extreme Value Theory	Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, Christine Largouet	Here, we propose a new approach to detect outliers in streaming univariate time series based on Extreme Value Theory that does not require to hand-set thresholds and makes no assumption on the distribution: the main parameter is only the risk, controlling the number of false positives.
121	Relay-Linking Models for Prominence and Obsolescence in Evolving Networks	Mayank Singh, Rajdeep Sarkar, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti	We propose a new temporal sketch of an evolving graph, and introduce several new characterizations of a network’s temporal dynamics.
122	PAMAE: Parallel	Hwanjun Song, Jae-Gil Lee, Wook-Shin Han	In this paper, we propose a novel parallel k-medoids algorithm, which we call PAMAE, that achieves both high accuracy and high efficiency.
123	Sparse Compositional Local Metric Learning	Joseph St.Amand, Jun Huan	Inspired by the recent resurgence of Frank-Wolfe style optimization, we propose a new method for sparse compositional local Mahalanobis distance metric learning.
124	End-to-end Learning for Short Text Expansion	Jian Tang, Yue Wang, Kai Zheng, Qiaozhu Mei	We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task.
125	Construction of Directed 2K Graphs	Bálint Tillman, Athina Markopoulou, Carter T. Butts, Minas Gjoka	We study the problem of generating synthetic graphs that resemble real-world directed graphs in terms of their degree correlations.
126	Optimized Risk Scores	Berk Ustun, Cynthia Rudin	In this paper, we present a principled approach to learn risk scores that are fully optimized for feature selection, integer coefficients, and operational constraints.
127	A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users	Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, Hui Xiong	In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users’ check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews.
128	Adversary Resistant Deep Neural Networks with an Application to Malware Detection	Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia, Xinyu Xing, Xue Liu, C. Lee Giles	As such, we propose a new adversary resistant technique that obstructs attackers from constructing impactful adversarial samples by randomly nullifying features within data vectors.
129	Multi-Modality Disease Modeling via Collective Deep Matrix Factorization	Qi Wang, Mengying Sun, Liang Zhan, Paul Thompson, Shuiwang Ji, Jiayu Zhou	In this paper, we propose a framework to fuse multiple data modalities for predictive modeling using deep matrix factorization, which explores the non-linear interactions among the modalities and exploits such interactions to transfer knowledge and enable high performance prediction.
130	Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models	Tianyi Wu, Shinya Sugawara, Kenji Yamanishi	We propose a new model selection criterion based on the minimum description length principle in a name of the decomposed normalized maximum likelihood criterion.
131	Structural Event Detection from Log Messages	Fei Wu, Pranay Anchuri, Zhenhui Li	We propose a novel method to mine structural events as directed workflow graphs (where nodes represent log patterns, and edges represent relations among patterns).
132	Retrospective Higher-Order Markov Processes for User Trails	Tao Wu, David F. Gleich	In this paper we propose the retrospective higher-order Markov process (RHOMP) as a low-parameter model for such sequences.
133	Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates	Liyang Xie, Inci M. Baytas, Kaixiang Lin, Jiayu Zhou	In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge.
134	Evaluating U.S. Electoral Representation with a Joint Statistical Model of Congressional Roll-Calls, Legislative Text, and Voter Registration Data	Zhengming Xing, Sunshine Hillygus, Lawrence Carin	Extensive information on 3 million randomly sampled United States citizens is used to construct a statistical model of constituent preferences for each U.S. congressional district.
135	Convex Factorization Machine for Toxicogenomics Prediction	Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang	We introduce the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
136	Distributed Local Outlier Detection in Big Data	Yizhou Yan, Lei Cao, Caitlin Kulhman, Elke Rundensteiner	In this work, we present the first distributed solution for the Local Outlier Factor (LOF) method — a popular outlier detection technique shown to be very effective for datasets with skewed distributions.
137	Scalable Top-n Local Outlier Detection	Yizhou Yan, Lei Cao, Elke A. Rundensteiner	In this work, we present the first scalable Top-N local outlier detection approach called TOLF.
138	Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation	Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, Jiawei Han	In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.
139	Multi-task Function-on-function Regression with Co-grouping Structured Sparsity	Pei Yang, Qi Tan, Jingrui He	In this paper, we propose a novel multi-task function-on-function regression approach to model both the functionality and heterogeneity of data.
140	Learning from Labeled and Unlabeled Vertices in Networks	Wei Ye, Linfei Zhou, Dominik Mautz, Claudia Plant, Christian Böhm	In this paper, we propose a semi-supervised learning framework called weighted-vote Geometric Neighbor classifier (wvGN) to infer the likely labels of unlabeled vertices in sparsely labeled networks.
141	Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach	Peifeng Yin, Ping Luo, Taiga Nakamura	In this work, we develop a batch-adaptive stochastic gradient descent (BA-SGD) algorithm, which can dynamically choose a proper batch size as learning proceeds.
142	Learning from Multiple Teacher Networks	Shan You, Chang Xu, Chao Xu, Dacheng Tao	In this paper, we present a method to train a thin deep network by incorporating multiple teacher networks not only in output layer by averaging the softened outputs (dark knowledge) from different networks, but also in the intermediate layers by imposing a constraint about the dissimilarity among examples.
143	A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics	Linyun Yu, Peng Cui, Chaoming Song, Tianyang Zhang, Shiqiang Yang	To tackle this challenge, we develop a temporal Heterogeneous Survival framework where the regularities in response time dimension and natural time dimension can be organically integrated.
144	Inductive Semi-supervised Multi-Label Learning with Co-Training	Wang Zhan, Min-Ling Zhang	In this paper, a novel approach named COINS is proposed to learning from labeled and unlabeled data by adapting the well-known co-training strategy which naturally works under inductive setting.
145	LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity	Yutao Zhang, Robert Chen, Jie Tang, Walter F. Stewart, Jimeng Sun	In this work, we propose the LEAP (LEArn to Prescribe) algorithm to decompose the treatment recommendation into a sequential decision-making process while automatically determining the appropriate number of medications.
146	Visualizing Attributed Graphs via Terrain Metaphor	Yang Zhang, Yusu Wang, Srinivasan Parthasarathy	In this article we propose a visualization method to explore attributed graphs with numerical attributes associated with nodes (or edges).
147	Achieving Non-Discrimination in Data Release	Lu Zhang, Yongkai Wu, Xintao Wu	In this paper, we show that the key to discrimination discovery and prevention is to find the meaningful partitions that can be used to provide quantitative evidences for the judgment of discrimination.
148	Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale	Adrian Albert, Jasleen Kaur, Marta C. Gonzalez	For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities.
149	Luck is Hard to Beat: The Difficulty of Sports Prediction	Raquel Y.S. Aoki, Renato M. Assuncao, Pedro O.S. Vaz de Melo	As another contribution of this paper, we propose a probabilistic graphical model to learn about the teams’ skills and to decompose the relative weights of luck and skill in each game.
150	Planning Bike Lanes based on Sharing-Bikes’ Trajectories	Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, Yu Zheng	In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data.
151	TFX: A TensorFlow-Based Production-Scale Machine Learning Platform	Denis Baylor, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, Mustafa Ispir, Vihan Jain, Levent Koc, Chiu Yuen Koo, Lukasz Lew, Clemens Mewald, Akshay Naresh Modi, Neoklis Polyzotis, Sukriti Ramesh, Sudip Roy, Steven Euijong Whang, Martin Wicke, Jarek Wilkiewicz, Xin Zhang, Martin Zinkevich	We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google.
152	LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace	Fedor Borisyuk, Liang Zhang, Krishnaram Kenthapadi	We present a dynamic forecasting model to estimate the expected number of applications at the job expiration date, and algorithms to either promote or penalize jobs based on the output of the forecasting model. In this paper, we propose the job application redistribution problem, with the goal of ensuring that job postings do not receive too many or too few applications, while still providing job recommendations to users with the same level of relevance.
153	A Data Science Approach to Understanding Residential Water Contamination in Flint	Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob Abernethy, Eric Schwartz	In this paper, we predict the lead contamination for each household’s water supply, and we study several related aspects of Flint’s water troubles, many of which generalize well beyond this one city.
154	Estimation of Recent Ancestral Origins of Individuals on a Large Scale	Ross E. Curtis, Ahna R. Girshick	In this study, we describe a classification method that leverages network features to assign individuals to communities in a large network corresponding to recent ancestry.
155	A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments	Pavel Dmitriev, Somit Gupta, Dong Woo Kim, Garnet Vaz	With this paper, we aim to increase the experimenters’ awareness of metric interpretation issues, leading to improved quality and trustworthiness of experiment results and better data-driven decisions.
156	A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations	Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang	In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015.
157	FIRST: Fast Interactive Attributed Subgraph Matching	Boxin Du, Si Zhang, Nan Cao, Hanghang Tong	In this paper, we propose a family of effective and efficient algorithms (FIRST) to support interactive attributed subgraph matching.
158	Prognosis and Diagnosis of Parkinson’s Disease Using Multi-Task Learning	Saba Emrani, Anya McGuirk, Wei Xiao	In this paper, we employ a multi-task learning regression framework for prediction of Parkinson’s disease progression, where each task is the prediction of PD rating scales at one future time point.
159	A Data Mining Framework for Valuing Large Portfolios of Variable Annuities	Guojun Gan, Jimmy Xiangji Huang	In this paper, we propose a novel data mining framework to address the computational issue associated with the valuation of large portfolios of variable annuity contracts.
160	GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources	Saurav Ghosh, Prithwish Chakraborty, Bryan L. Lewis, Maimuna S. Majumder, Emily Cohn, John S. Brownstein, Madhav V. Marathe, Naren Ramakrishnan	In this paper, we motivate Guided Epidemiological Line List (GELL), the first tool for building automated line lists (in near real-time) from open source reports of emerging disease outbreaks.
161	Google Vizier: A Service for Black-Box Optimization	Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley	In this paper we describe Google Vizier, a Google-internal service for performing black-box optimization that has become the de facto parameter tuning engine at Google.
162	Predicting Clinical Outcomes Across Changing Electronic Health Record Systems	Jen J. Gong, Tristan Naumann, Peter Szolovits, John V. Guttag	We demonstrate our method on machine learning models developed in a healthcare setting.
163	HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network	Shifu Hou, Yanfang Ye, Yangqiu Song, Melih Abdulhayoglu	We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN).
164	Peeking at A/B Tests: Why it matters, and what to do about it	Ramesh Johari, Pete Koomen, Leonid Pekelis, David Walsh	This paper reports on novel statistical methodology, which has been deployed by the commercial A/B testing platform Optimizely to communicate experimental results to their customers.
165	PNP: Fast Path Ensemble Method for Movie Design	Danai Koutra, Abhilash Dighe, Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis, Christos Faloutsos, Jean Bolot	In this work, we seek to identify how we can design new movies with features tailored to a specific user population.
166	Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data	Zhaobin Kuang, Peggy Peissig, Vitor Santos Costa, Richard Maclin, David Page	Inspired by the Multiple Self-Controlled Case Series (MSCCS) model, arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times.
167	FLAP: An End-to-End Event Log Analysis Platform for System Management	Tao Li, Yexi Jiang, Chunqiu Zeng, Bin Xia, Zheng Liu, Wubai Zhou, Xiaolong Zhu, Wentao Wang, Liang Zhang, Jun Wu, Li Xue, Dewei Bao	In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs.
168	Cascade Ranking for Operational E-commerce Search	Shichen Liu, Fei Xiao, Wenwu Ou, Luo Si	The challenge of the real-world application provides new insights for research: 1).
169	Developing a Comprehensive Framework for Multimodal Feature Extraction	Quinten McNamara, Alejandro De La Vega, Tal Yarkoni	To address this challenge, we introduce a new open-source framework for comprehensive multimodal feature extraction.
170	Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction	Alejandro Mottini, Rodrigo Acuna-Agost	In this paper, we concentrate with the problem of modeling air passenger choices of flight itineraries.
171	Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says!	Debjyoti Paul, Feifei Li, Murali Krishna Teja, Xin Yu, Richie Frost	This paper investigates this problem through a data science project called "US Election 2016, What Twitter Says".
172	Backpage and Bitcoin: Uncovering Human Traffickers	Rebecca S. Portnoff, Danny Yuxing Huang, Periwinkle Doerfler, Sadia Afroz, Damon McCoy	In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad).
173	Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data	Paul Power, Hector Ruiz, Xinyu Wei, Patrick Lucey	In this paper, we show how we estimate both the risk and reward of a pass across two seasons of tracking data captured from a recent professional soccer league with state-of-the-art performance, then showcase various use cases of our deployed passing system.
174	MARAS: Signaling Multi-Drug Adverse Reactions	Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Elke A. Rundensteiner, Lei Cao	In this research, we design a Multi-Drug Adverse Reaction Analytics Strategy, called MARAS, to signal severe unknown ADRs triggered by the usage of a combination of drugs, also known as Multi-Drug Adverse Reactions (MDAR).
175	A Practical Exploration System for Search Advertising	Parikshit Shah, Ming Yang, Sachidanand Alle, Adwait Ratnaparkhi, Ben Shahshahani, Rohit Chandra	In this paper, we describe an exploration system that was implemented by the search-advertising team of a prominent web-portal to address the cold ads problem.
176	MOLIERE: Automatic Biomedical Hypothesis Generation System	Justin Sybrandt, Michael Shtutman, Ilya Safro	We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network.
177	Quick Access: Building a Smart Experience for Google Drive	Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith, Divanshu Garg, Cayden Meyer, Reuben Kan	Quick Access: Building a Smart Experience for Google Drive
178	The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms	Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv	To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features.
179	DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution	Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani, Auroop R. Ganguly	In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables.
180	No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods	Jingyuan Wang, Chao Chen, Junjie Wu, Zhang Xiong	In this paper, we present a novel system called DGeye, which features a "duet" between DGT trajectory data and human mobility data for risky zones identification.
181	A Quasi-experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns	Zhe Zhang, Beibei Li	To gain insights about future impact of urban transportation changes, in this paper, we utilize a novel dataset and econometric analysis methods to present a quasi-experimental examination of how the emerging growth of peer-to-peer car sharing services may have affected local consumer mobility and consumption patterns.
182	KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial	Jun Zhou, Xiaolong Li, Peilin Zhao, Chaochao Chen, Longfei Li, Xinxing Yang, Qing Cui, Jin Yu, Xu Chen, Yi Ding, Yuan Alan Qi	This motivated us to design a universal distributed platform termed KunPeng, that combines both distributed systems and parallel optimization algorithms to deal with the complexities that arise from large-scale ML.
183	Deep Embedding Forest: Forest-based Serving with Deep Embedding Features	Jie Zhu, Ying Shan, J.C. Mao, Dong Yu, Holakou Rahmanian, Yi Zhang	This work proposes a Deep Embedding Forest model that benefits from the best of both worlds.
184	A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications	Amr Ahmed, James Long, Daniel Silva, Yuan Wang	In this paper we take a radically different approach to incorporating word-word correlation in topic models by applying this side information at the posterior level rather than at the prior level.
185	Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity	Blake Anderson, David McGrew	In this paper, we highlight two primary reasons why this is the case: inaccurate ground truth and a highly non-stationary data distribution.
186	Extremely Fast Decision Tree Mining for Evolving Data Streams	Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer	In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei.
187	Real-Time Optimization of Web Publisher RTB Revenues	Pedro Chahuara, Nicolas Grislain, Gregoire Jauvion, Jean-Michel Renders	This paper describes an engine to optimize web publisher revenues from second-price auctions.
188	Customer Lifetime Value Prediction Using Embeddings	Benjamin Paul Chamberlain, Ângelo Cardoso, C.H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth	We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer.
189	TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks	Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie	We present a framework for specifying, training, evaluating, and deploying machine learning models.
190	Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices	Hamid Dadkhahi, Benjamin M. Marlin	In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes.
191	AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments	Supratim Deb, Zihui Ge, Sastry Isukapalli, Sarat Puthenpura, Shobha Venkataraman, He Yan, Jennifer Yates	In this paper, we present AESOP, a data-driven intelligent system to facilitate automatic learning of policies and rules for triggering remedial actions in networks.
192	Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem	Shalini Ghosh, Ariyam Das, Phil Porras, Vinod Yegneswaran, Ashish Gehani	In this paper we describe Automated Tool for Onion Labeling (ATOL), a novel scalable analysis service developed to conduct a thematic assessment of the content of onion sites in the LIGHTS repository.
193	Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster	Naeemul Hassan, Fatma Arslan, Chengkai Li, Mark Tremayne	This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses.
194	An Efficient Bandit Algorithm for Realtime Multivariate Optimization	Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S.V.N. Vishwanathan	We formulate an approach where the possible interactions between different components of the page are modeled explicitly.
195	Large Scale Sentiment Learning with Limited Labels	Vasileios Iosifidis, Eirini Ntoutsi	Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data.
196	Optimization Beyond Prediction: Prescriptive Price Optimization	Shinji Ito, Ryohei Fujimaki	We present that the optimization problem can be formulated as an instance of binary quadratic programming (BQP).
197	Finding Precursors to Anomalous Drop in Airspeed During a Flight’s Takeoff	Vijay Manikandan Janakiraman, Bryan Matthews, Nikunj Oza	In this paper, we present our work on finding precursors to the anomalous drop-in-airspeed (ADA) event using the ADOPT (Automatic Discovery of Precursors in Time series) algorithm.
198	Ad Serving with Multiple KPIs	Brendan Kitts, Michael Krishnan, Ishadutta Yadav, Yongbo Zeng, Garrett Badeau, Andrew Potter, Sergey Tolkachov, Ethan Thornburg, Satyanarayana Reddy Janga	We hypothesize that advertisers may be defining these metrics to create a kind of "proxy target".
199	Discovering Pollution Sources and Propagation Patterns in Urban Area	Xiucheng Li, Yun Cheng, Gao Cong, Lisi Chen	In this work, we propose the first solution for the problem, which comprises two steps.
200	Discovering Enterprise Concepts Using Spreadsheet Tables	Keqian Li, Yeye He, Kris Ganjam	In this work, we study the problem of building concept hierarchies using a large corpus of enterprise spreadsheet tables.
201	Supporting Employer Name Normalization at both Entity and Cluster Level	Qiaoling Liu, Faizan Javed, Vachik S. Dave, Ankita Joshi	To address these challenges, in this paper we extend the previous CompanyDepot system to normalize employer names not only at entity level, but also at cluster level by mapping a query to a cluster in the KB that best matches the query.
202	BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency	Yin Lou, Mikhail Obukhov	In this paper we present gradient boosted decision tables (BDTs).
203	Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks	Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao	To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients’ future health information.
204	Internet Device Graphs	Matthew Malloy, Paul Barford, Enis Ceyhun Alp, Jonathan Koller, Adria Jewell	We report on the characteristics of the graph and the communities.
205	RUSH!: Targeted Time-limited Coupons via Purchase Forecasts	Emaad Manzoor, Leman Akoglu	In this work, we consider the problem of delivering time-limited discount coupons, where we partner with a large national bank functioning as a commission-based third-party coupon provider.
206	Embedding-based News Recommendation for Millions of Users	Shumpei Okura, Yukihiro Tagami, Shingo Ono, Akira Tajima	Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.
207	Learning to Count Mosquitoes for the Sterile Insect Technique	Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel Newburger, Ryan Poplin, Tiantian Zha, D. Sculley	This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise.
208	An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis	Lujia Pan, Jianfeng Zhang, Patrick P.C. Lee, Hong Cheng, Cheng He, Caifeng He, Keli Zhang	We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis.
209	Deep Design: Product Aesthetics for Heterogeneous Markets	Yanxin Pan, Alexander Burnap, Jeffrey Hartley, Richard Gonzalez, Panos Y. Papalambros	We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design.
210	Collecting and Analyzing Millions of mHealth Data Streams	Tom Quisel, Luca Foschini, Alessio Signorini, David C. Kale	In this work, we describe the infrastructure Evidation Health has developed to collect mHealth data from millions of users through hundreds of different mobile devices and apps.
211	Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines	Kosta Ristovski, Chetan Gupta, Kunihiko Harada, Hsiu-Khuern Tang	To address this problem, we have implemented truck assignment approach which integrates machine learning, linear/integer programming and simulation.
212	"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons	Hector Ruiz, Paul Power, Xinyu Wei, Patrick Lucey	"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons
213	Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach	Hesam Salehian, Patrick Howell, Chul Lee	We propose a novel, practical, and scalable machine learning solution architecture, consisting of two major steps.
214	The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue	Ashlesh Sharma, Vidyuth Srinivasan, Vishal Kanchan, Lakshminarayanan Subramanian	In this paper, we introduce a new mechanism that uses machine learning algorithms on microscopic images of physical objects to distinguish between genuine and counterfeit versions of the same product.
215	Automatic Application Identification from Billions of Files	Kyle Soska, Chris Gates, Kevin A. Roundy, Nicolas Christin	In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably.
216	Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention	Bin Tong, Martin Klinkigt, Makoto Iwayama, Toshihiko Yanase, Yoshiyuki Kobayashi, Anshuman Sahu, Ravigopal Vennelakanti	In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions.
217	Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes	Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi	In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography (OCT).
218	Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration	Xuejian Wang, Lantao Yu, Kan Ren, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang	In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool.
219	A Hybrid Framework for Text Modeling with Convolutional RNN	Chenglong Wang, Feijun Jiang, Hongxia Yang	In this paper, we introduce a generic inference hybrid framework for Convolutional Recurrent Neural Network (conv-RNN) of semantic modeling of text, seamless integrating the merits on extracting different aspects of linguistic information from both convolutional and recurrent neural network structures and thus strengthening the semantic understanding power of the new framework.
220	Formative Essay Feedback Using Predictive Scoring Models	Bronwyn Woods, David Adamson, Shayne Miel, Elijah Mayfield	Extending this model, we describe a method for using prediction on realistic essay variants to give rubric-specific formative feedback to writers.
221	Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data	Houping Xiao, Jing Gao, Long Vu, Deepak S. Turaga	In this paper, we propose a novel framework to capture the trajectory of latent states for patients from behavioral data while exploiting their demographic differences and similarities to other patients.
222	Local Algorithm for User Action Prediction Towards Display Ads	Hongxia Yang, Yada Zhu, Jingrui He	Our goal is to improve action prediction by leveraging historical user-user, campaign-campaign, and user-campaign interactions.
223	Visual Search at eBay	Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, Robinson Piramuthu	In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure.
224	A Data-driven Process Recommender Framework	Sen Yang, Xin Dong, Leilei Sun, Yichen Zhou, Richard A. Farneth, Hui Xiong, Randall S. Burd, Ivan Marsic	We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations.
225	Predicting Optimal Facility Location without Customer Locations	Emre Yilmaz, Sanem Elbasi, Hakan Ferhatosmanoglu	In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known.
226	DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks	Zi Yin, Keng-hao Chang, Ruofei Zhang	In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network.
227	Stock Price Prediction via Discovering Multi-Frequency Trading Patterns	Liheng Zhang, Charu Aggarwal, Guo-Jun Qi	To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time.
228	A Taxi Order Dispatch Model based On Combinatorial Optimization	Lingyu Zhang, Tao Hu, Yue Min, Guobin Wu, Junying Zhang, Pengcheng Feng, Pinghua Gong, Jieping Ye	In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings.
229	Contextual Spatial Outlier Detection with Metric Learning	Guanjie Zheng, Susan L. Brantley, Thomas Lauvaux, Zhenhui Li	Specifically, we propose a spatial outlier detection method based on contextual neighbors.
230	Resolving the Bias in Electronic Medical Records	Kaiping Zheng, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Wei Luen James Yip	To this end, we propose a general method to resolve the bias by transforming EMR to regular patient hidden condition series using a Hidden Markov Model (HMM) variant.
231	STAR: A System for Ticket Analysis and Resolution	Wubai Zhou, Wei Xue, Ramesh Baral, Qing Wang, Chunqiu Zeng, Tao Li, Jian Xu, Zheng Liu, Larisa Shwartz, Genady Ya. Grabarnik	The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features.
232	Optimized Cost per Click in Taobao Display Advertising	Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, Kun Gai	Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity.