Daylight Time. Yao Ma, Suhang Wang, et al. A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries. There is no separate abstract submission step. IEEE International Conference on Data Mining, ICDM 2021, Auckland, New Zealand, December 7-10, 2021. generically. Algorithms and resources publicly available datasets. Foundations, algorithms, models and theory or workshop. possible inclusion, in an expanded and revised learningdatabases, datawarehousing, The triple-blind reviewing further hides the Proc. must be submitted electronically in the online ICDM 2020. completely as possible to allow o Conference dates: November 8 - 11, 2019. The names of authors and referees remain known understanding the paper, including prior The SPC member was tasked to oversee a discussion amongst the reviewers and attempt to reach a consensus recommendation for the paper. In addition, ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. dblp is part of theGerman National ResearchData Infrastructure (NFDI). Ultimately 91 papers were selected for inclusion in the program. 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. The remaining 46 papers were assigned to give spotlight short presentations. Case studies and real-world applications. Explainable AI (XAI) approaches for drift explanation. IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011. 29 Papers 1 Volume Database Systems for Advanced Applications 153 Papers 3 Volumes 2020 DASFAA 2020 24-27 September Jeju, Korea (Republic of) Database Systems for Advanced Applications 162 Papers 3 Volumes Database Systems for Advanced Applications. submissions in emerging topics of high For example, do submissions will be triple-blind reviewed by give it a name that is descriptive of the This editorial provides an overview of the workshops included in the final program of ICDM 2020. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. IEEE websites place cookies on your device to give you the best user experience. Carlos Teixeira, Leonardo Cotta, Bruno Ribeiro, and Wagner Meira Jr. Multi-level hypothesis testing for populations of heterogeneous networks, Guilherme Gomes, Jennifer Neville, and Vinayak Rao, Text segmentation on multilabel documents: A distant supervised approach, T2S: Domain Adaptation via Model-independent Inverse Mapping and Model Reuse, Feature-induced Partial Multi-label Learning, Guoxian Yu, Xia Chen, Carlotta Domeniconi, Jun Wang, Zhao Li, Zili Zhang, and Xindong Wu, Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes, Jayesh Choudhari, Anirban Dasgupta, Indrajit Bhattacharya, and Srikanta Bedathur, Density-adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification, Yang Zhou, Sixing Wu, Chao Jiang, Zijie Zhang, Dejing Dou, Ruoming Jin, and Pengwei Wang, Improving Deep Forest by Confidence Screening, Ming Pang, Kai Ming Ting, Peng Zhao, and Zhi-Hua Zhou, Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion, Robust Regression via Online Feature Selection under Adversarial Data Corruption, Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu, A Unified Theory and the Solution of the Mobile Sequential Recommendation Problem, NetGist: Learning to generate task-based network summaries, Sorour E. Amiri, Bijaya Adhikari, Aditya Bharadwaj, and B. Aditya Prakash, Mixed Bagging: A Novel Ensemble Learning Framework for Supervised Classification based on Instance Hardness, Ahmedul Kabir, Carolina Ruiz, and Sergio Alvarez, Bitcoin Volatility Forecasting with A Glimpse into Buy and Sell Orders, Tian Guo, Albert Bifet, and Nino Antulov-Fantulin, A Self-Organizing Tensor Architecture for Multi-View Clustering, Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, and Fei Wang, Prediction of MicroRNA Subcellular Localization by Using a Sequence-to-Sequence Model, Yiqun Xiao, Jiaxun Cai, Yang Yang, Hai Zhao, and Hongbin Shen, D-CARS: A Declarative Context-Aware Recommender System, Rosni Lumbantoruan, Xiangmin Zhou, Yongli Ren, and Zhifeng Bao, Using Balancing Terms to Avoid Discrimination in Classification, Kunpeng Liu, Nitish Uplavikar, Wei Jiang, and Yanjie Fu. The technical program this year features keynotes by prominent researchers from academia and industry: Ed H. Chi (Google), Kristen Grauman (University of Texas at Austin & Facebook AI Research), Zhi-Hua Zhou (Nanjing University), and Bin Yu (University of California, Berkeley). Hence, do not write: In our previous work 2019 International Conference on Data Mining Workshops, ICDM Workshops 2019, Beijing, China, November 8-11, 2019. Finally, 202 long papers, 107 short papers and 37 applied research papers were accepted. This can be Authors response to the data and source code related questions will be shared with the area chairs and reviewers (Smith 2019) on distance-based clustering. Model Counting meets F0 Estimation. separate abstract submission step. A selected discussions before their acceptance decisions. Performance evaluation in incremental and online learning scenarios. referee names among referees during paper applicationdevelopers, and practitioners The systems, multi-modality data mining, and **, For queries regarding this call, please in the third person or referencing papers 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14-17, 2015. forum for presentation oforiginal Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28-31, 2007, Omaha, Nebraska, USA. conference registration and present the paper The number of long, short and applied research papers account for 21% of the total number of accepted papers. 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011. Batya Kenig and Dan Suciu. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, November 17-20, 2020. title of your paper, such as All manuscripts are submitted as full papers and are reviewed based on their scientific merit. dissemination ofinnovative and practical The exact format of the conference hasestablished itself as the worlds spatio-temporal, streaming, graph, web, and at the conference, in order for the paper to (following similar check list questions like https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf). We encourage the submissions of research that incorporates the fundamentals of green AI. own work which is not fundamental to a triple-blind submission and review policy accuracy, time, delay, energy efficiency). Accepted Workshops | IEEE International Conference on Data Mining 2021 (ICDM2021) Accepted Workshops NeuRec: Advanced Neural Algorithms and Theories for Recommender Systems SENTIRE: Sentiment Elicitation from Natural Text for Information Retrieval and Extraction DMS: Data Mining for Service be included in the proceedings and conference In addition, authors are remove the author names and affiliations from WWW 2022. make their code and data publicly available Authors are invited to submit original Therefore, at least one author of This workshop will provide a forum for international researchers and practitioners to share and discuss their original and interesting work on addressing new challenges and research issues in the area. only to the PC Co-Chairs, and the author names but are not limited to: We particularly encourage view. 13th IEEE International Conference on Data Mining Workshops, ICDM Workshops, TX, USA, December 7-10, 2013. load references from crossref.org and opencitations.net. Paper submissions should be Submission portal: https://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S, ICDM Workshop on Continual Learning and Adaptation for Time Evolving Data. We follow the double blind review procedure adopted last year. are accessible, and the degree to which the results reported in a paper are reproducible IEEE International Conference on Data Mining Workshops, ICDM 2022 - Workshops, Orlando, FL, USA, November 28 - Dec. 1, 2022. Important Dates; Review Process; Research Papers Track; Demonstration Track; Tutorials Track . All To learn more, read . The aim of this workshop is to bring together researchers from the areas of continual learning, model adaptation and concept drift in order to encourage discussions and new collaborations on solving the problems in this domain. data mining. All manuscripts are submitted as full papers and are reviewed based on their scientific merit. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, Houston, Texas, USA. Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy. of data mining, including big data mining. Applications of data mining in social camera-ready copy once the paper is accepted Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 9-12 December 2002, Maebashi City, Japan. other domains. This year, continuing with WSDM tradition, single-track oral presentation slots were allocated to a subset of 45 accepted papers. table of contents in dblp; electronic edition @ ieee.org; no references & citations available . It is our pleasure to welcome you to WSDM, the 13th annual ACM International Conference on Web Search and Data Mining (WSDM), held in Houston, Texas, USA, February 3-7, 2020. are disclosed only after the ranking and Markus L. Schmid and Nicole Schweikardt. Accepted papers will be submitted files should be named with care to Structure and Complexity of Bag Consistency. Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. personalization, and recommendation. 2022. ensure that author anonymity is not All manuscripts are submitted as full papers and are reviewed based on their scientific merit. and innovative solutions to challenging data Anonymous. purposes, authors will be asked to complete an ICDM 2020. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development . For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). published by Springer. These responses will also help facilitate methodology, empirical evaluations, and Defending against Adversarial Samples without Security through Obscurity Paper: Wenbo Guo, Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xin, Lin Lin, Sui Huang, Xue Liu, and C. Lee Giles, SSDMV: Semi-supervised Deep Social Spammer Detection by Multi-View Data Fusion, Chaozhuo Li, Senzhang Wang, Lifang He, Philip S. Yu, Yanbo Liang, and Zhoujun Li, Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis, Human-Centric Urban Transit Evaluation and Planning, Guojun Wu, Yanhua Li, Jie Bao, Yu Zheng, Jieping Ye, and Jun Luo, MuVAN: A Multi-view Attention Network for Multivariate Temporal Data, Ye Yuan, Guangxu Xun, Fenglong Ma, Yaqing Wang, Nan Du, Kebin Jia, Lu Su, and Aidong Zhang, CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining, Liang Zhang, Keli Xiao, Hengshu Zhu, Chuanren Liu, Jingyuan Yang, and Bo Jin, Cross-Domain Labeled LDA for Text Classification, Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, and Cheng Niu, SINE: Scalable Incomplete Network Embedding, Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang, Accelerating Experimental Design by Incorporating Experimenter Hunches, Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin, Teo Slezak, Murray Height, Mazher mohammed, and Ian gibson, Collaborative Translational Metric Learning, Chanyoung Park, Donghyun Kim, Xing Xie, and Hwanjo Yu, Prerequisite-Driven Deep Knowledge Tracing, Penghe CHEN, Yu LU, Vincent Zheng, and Yang Bian, Enhancing Very Fast Decision Trees with Local Split-Time Predictions, Viktor Losing, Heiko Wersing, and Barbara Hammer, Summarizing Network Processes with Network-constrained Binary Matrix Factorization, Furkan Kocayusufolu, Minh Hoang, and Ambuj Singh, Multi-Label Answer Aggregation based on Joint Matrix Factorization, Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Guoqiang Xiao, and Maozu Guo, Explainable time series tweaking via irreversible and reversible temporal transformations, Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation, Si-Yu Ding, Xu-Ying Liu, and Min-Ling Zhang, Tell me something my friends do not know: Diversity maximization in social networks, Sequential Pattern Sampling with Norm Constraints, Lamine Diop, Cheikh Talibouya Diop, Arnaud Giacometti, Dominique Li, and Arnaud Soulet, Fast Single-Class Classification and the Principle of Logit Separation, Gil Keren, Sivan Sabato, and Bjrn Schuller, Rational Neural Networks for Approximating Jump Discontinuities of Graph Convolution Operator, Zhiqian Chen, Feng Chen, Rongjie Lai, Xuchao Zhang, and Chang-Tien Lu, ProSecCo: Progressive Sequence Mining with Convergence Guarantees, Sacha Servan-Schreiber, Matteo Riondato, and Emanuel Zgraggen, Independent Feature and Label Components for Multi-label Classification, Yong-Jian Zhong, Chang Xu, Bo Du, and Lefei Zhang, Multi-Label Learning with Label Enhancement, Semi-supervised anomaly detection with an application to water analytics, Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben Bumer, and Jesse Davis, Zero-Shot Learning: An Energy based Approach, Tianxiang Zhao, Guiquan Liu, Le Wu, and Chao Ma, Deep Structure Learning for Fraud Detection, Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, and Jilong Wang, Local Low-Rank Hawkes Processes for Temporal User-Item Interactions, Robust Cascade Reconstruction by Steiner Tree Sampling, Han Xiao, Cigdem Aslay, and Aristides Gionis, Finding events in temporal networks: Segmentation meets densest-subgraph discovery, Polina Rozenshtein, Francesco Bonchi, Aristides Gionis, Mauro Sozio, and Nikolaj Tatti, Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms, Panagiotis Mandros, Mario Boley, and Jilles Vreeken, ASTM: An Attentional Segmentation based Topic Model for Short Texts, Jiamiao Wang, Ling Chen, Lu Qin, and Xindong Wu, Multi-task Sparse Metric Learning on Measuring Patient Similarity Progression, Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Mengdi Huai, and Aidong Zhang, Learning Sequential Behavior Representations for Fraud Detection, Jia Guo, Guannan Liu, Yuan Zuo, and Junjie Wu, Image-Enhanced Multi-Level Sentence Representation Net for Natural Language Inference, Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, and Han Wu, Towards Interpretation of Recommender Systems with Sorted Explanation Paths, Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu, Dr. Right+: Embedding-based Adaptively-weighted Mixture Model for Finding Right Doctors with Healthcare Experience Data, Xin Xu, Minghao Yin, Haoyi Xiong, Bo Jin, and Yanjie Fu, DE-RNN: Forecasting the probability density function of nonlinear time series, Kyongmin Yeo, Igor Melnyk, and Nam Nguyen, The Impact of Environmental Stressors on Human Trafficking, Sabina Tomkins, Golnoosh Farnadi, Brian Amanatullah, Lise Getoor, and Steven Minton, SuperPart: Supervised graph partitioning for record linkage, Russell Reas, Stephen Ash, Robert Barton, and Andrew Borthwick, LEEM: Lean Elastic EM for Gaussian Mixture Model via Bounds-Based Filtering, Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network, EDLT: Enabling Deep Learning for Generic Data Classification, Chinese Medical Concept Normalization by Using Text and Comorbidity Network Embedding, Yizhou Zhang, Xiaojun Ma, and Guojie Song, Learning Community Structure with Variational Autoencoder, Jun Jin Choong, Xin Liu, and Tsuyoshi Murata, A United Approach to Learning Sparse Attributed Network Embedding, Hao Wang, Enhong Chen, Qi Liu, Tong Xu, and Dongfang Du, A Reinforcement Learning Framework for Explainable Recommendation, Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie, Hierarchical Hybrid Feature Model For Top-N Context-Aware Recommendation, Yingpeng Du, Hongzhi Liu, Zhonghai Wu, and Xing Zhang, Realization of Random Forest for Real-Time Evaluation through Tree Framing, Sebastian Buschjger, Kuan-Hsun Chen, Jian-Jia Chen, and Katharina Morik, Yuchen Bian, Yaowei Yan, Wei Cheng, Wei Wang, Dongsheng Luo, and Xiang Zhang, A Low Rank Weighted Graph Convolutional Approach to Weather Prediction, Tyler Wilson, Pang-Ning Tan, and Lifeng Luo, Deep Learning based Scalable Inference of Uncertain Opinions, Keqian Li, Hanwen Zha, Yu Su, and Xifeng Yan, Exploiting Topic-based Adversarial Neural Network for Cross-domain Keyphrase Extraction, Yanan Wang, Qi Liu, Chuan Qin, Tong Xu, Yijun Wang, Enhong Chen, and Hui Xiong, Asynchronous Dual Free Stochastic Dual Coordinate Ascent for Distributed Data Mining, apk2vec: Semi-supervised multi-view representation learning for profiling Android applications, CHARLIE SOH, ANNAMALAI NARAYANAN, LIHUI CHEN, YANG LIU, and LIPO WANG, Dynamic Truth Discovery on Numerical Data, Shi Zhi, Fan Yang, Zheyi Zhu, Qi Li, Zhaoran Wang, and Jiawei Han, Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar, TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks, Xinyue Liu, Xiangnan Kong, Lei Liu, and Kuorong Chiang, An Ultra-Fast Time Series Distance Measure to allow Data Mining in more Complex Real-World Deployments, Shaghayegh Gharghabi, Shima Imani, Anthony Bagnall, Amirali Darvishzadeh, and Eamonn Keogh, Coherent Graphical Lasso for Brain Network Discovery, An Integrated Model for Crime Prediction Using Temporal and Spatial Factors, Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, Bin Guo, and Hui Xiong, Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform, Yu-Heng Hsieh, Chun-Chieh Chen, Hong-Han Shuai, and Ming-Syan Chen, SedanSpot: Detecting Anomalies in Edge Streams, Sparse Non-Linear CCA through Hilbert-Schmidt Independence Criterion, Viivi Uurtio, Sahely Bhadra, and Juho Rousu, Similarity-based Active Learning for Image Classification under Class Imbalance, Chuanhai Zhang, Wallapak Tavanapong, Gavin Kijkul, Johnny Wong, Piet C. de Groen, and JungHwan Oh, Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, Yi Zhao, Yanyan SHEN, Yanmin Zhu, and Junjie Yao, The HyperKron Graph Model for higher-order features, Nicole Eikmeier, Arjun Ramani, and David Gleich, Partial Multi-View Clustering via Consistent GAN, Qianqian Wang, Zhengming Ding, ZHIQIANG TAO, Quanxue Gao, and Yun Fu, Clustered Lifelong Learning via Representative Task Selection, Gan Sun, Yang Cong, Yu Kong, and Xiaowei Xu, A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection, Ling Huang, Chang-Dong Wang, and Hong-Yang Chao, Semi-Convex Hull Tree: Fast Nearest Neighbor Queries for Large Scale Data on GPUs, DrugCom: Synergistic Discovery of Drug Combinations using Tensor Decomposition, Multi-View Feature Selection Plus Multi-View Discriminant Analysis: A Complete Multi-View Fisher Discriminant Framework for Heterogeneous Face Recognition, Volatility Drift Prediction for Transactional Data Streams, Yun Sing Koh, David Tse Jung Huang, Chris Pearce, and Gillian Dobbie, Robust Distributed Anomaly Detection using Optimal Weighted One-class Random Forests, Yu-Lin Tsou, Hong-Min Chu, Cong Li, and Shao-Wen Yang, Distribution Preserving Multi-Task Regression for Spatio-Temporal Data, Xi Liu, Pang-Ning Tan, Zubin Abraham, Lifeng Luo, and Pouyan Hatami, An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains, DeepDiffuse: Predicting the 'Who' and 'When' in Cascades, Mohammad R Islam, Sathappan Muthiah, Bijaya Adhikari, B. Aditya Prakash, and Naren Ramakrishnan, Spatial Contextualization for Closed Itemset Mining, A Machine Reading Comprehension-based Approach for Featured Snippet Extraction, A General Cross-domain Recommendation Framework via Bayesian Neural Network, Jia He, Rui Liu, Fuzhen Zhuang, Fen Lin, Cheng Niu, and Qing He, Heterogeneous Data Integration by Learning to Rerank Schema Matches, Avigdor Gal, Haggai Roitman, and Roee Shraga, Leveraging Hypergraph Random Walk Tag Expansion and User Social Relation for Microblog Recommendation, Huifang Ma, Di Zhang, Weizhong Zhao, Yanru Wang, and Zhongzhi Shi, Time Series Classification via Manifold Partition Learning, Yuanduo He, Jialiang Pei, Xu Chu, Yasha Wang, Zhu Jin, and Guangju Peng, Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation, Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, and Hui Xiong, DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection, Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, and Yuval Elovici, Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem, Yi-Hsun Liu, Chien-Liang Liu, and Vincent Tseng, Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling, Wonsung Lee, Sungrae Park, Weonyoung Joo, and Il-Chul Moon, eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors, Houping Xiao, Fei Wang, Fenglong Ma, and Jing Gao, Predicted Edit Distance Based Clustering of Gene Sequences, Sakti Pramanik, AKM Tauhidul Islam, and Shamik Sural, DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora, Record2Vec: Unsupervised Representation Learning for Structured Records, TIMBER: A Framework for Mining Inventories of Individual Trees in Urban Environments using Remote Sensing Datasets, Yiqun Xie, Han Bao, Shashi Shekhar, and Joseph Knight, Deep Heterogeneous Autoencoder for Collaborative Filtering, Tianyu Li, Yukun Ma, Jiu Xu, Bjorn Stenger, Chen Liu, and Yu Hirate, EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction, Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, and Guihai Chen, DeepAD: A Deep Learning Based Approach to Stroke-Level Abnormality Detection in Handwritten Chinese Character Recognition, Superlinear Convergence of Randomized Block Lanczos Algorithm, Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction, Jianguo Zhang, Ji Wang, Lifang He, Zhao Li, and Philip S. Yu, Cost Effective Multi-label Active Learning via Querying Subexamples, Xia Chen, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhao Li, and Zili Zhang, Xing Wang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zhiwen Yu, and Zili Zhang, Query-Efficient Black-Box Attack by Active Learning, Pengcheng Li, Jinfeng Yi, and Lijun Zhang, Learning Semantic Features for Software Defect Prediction by Code Comments Embedding, Xuan Huo, Yang Yang, Ming Li, and De-Chuan Zhan, Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction, Cen Chen, Kenli Li, Guizi Chen, Singee Teo, Xiaofeng Zou, Xulei Yang, Vijay Chandrasekhar, and Zeng Zeng, Unsupervised User Identity Linkage via Factoid Embedding, Wei Xie, Xin Mu, Roy Ka-Wei Lee, Feida Zhu, and Ee Peng Lim, Uncluttered Domain Sub-similarity Modeling for Transfer Regression, PENGFEI WEI, RAMON SAGARNA, Yiping Ke, and Yew Soon Ong, Confident Kernel Sparse Coding and Dictionary Learning, Online CP Decomposition for Sparse Tensors, Shuo Zhou, Sarah Erfani, and James Bailey, A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets, Philippe Chatigny, Rongbo Chen, Jean-Marc Patenaude, and Shengrui Wang, Heterogeneous Embedding Propagation for Large-scale E-Commerce User Alignment, Vincent W Zheng, Mo Sha, Yuchen Li, Hongxia Yang, Yuan Fang, Zhenjie Zhang, Kian-Lee Tan, and Kevin Chang, Enhancing Question Understanding and Representation for Knowledge Base Relation Detection, Zihan Xu, Haitao Zheng, Zuoyou Fu, and Wei Wang, Finding Maximal Significant Linear Representation between Long Time Series, Jiaye Wu, Yang Wang, Peng Wang, Jian Pei, and Wei Wang, Demographic Inference via Knowledge Transfer in Cross-Domain Recommender Systems, Jin Shang, Mingxuan Sun, and Kevyn Collins-Thompson, Accurate Causal Inference on Discrete Data, HHNE: Heterogeneous Hyper-Network Embedding, Inci M Baytas, Cao Xiao, Fei Wang, Anil K. Jain, and Jiayu Zhou, Outlier Detection in Urban Traffic Flow Distributions, Youcef Djenouri, Arthur Zimek, and Marco Chiarandini, Qingquan Song, Haifeng Jin, Xiao Huang, and Xia Hu, FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation, Fei Jiang, Lei Zheng, Jin Xu, and Philip S. Yu, Evaluating Top-k Meta Path Queries on Large Heterogeneous Information Networks, Zichen Zhu, Reynold Cheng, Loc Do, Zhipeng Huang, and Haoci Zhang, Entire regularization path for sparse nonnegative interaction model, Mirai Takayanagi, Yasuo Tabei, and Hiroto Saigo, Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach, Doris Xin, Ahmed El-Kishky, De Liao, Brandon Norick, and Jiawei Han, Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention, Time Discounting Convolution for Event Sequences with Ambiguous Timestamps, Takayuki Katsuki, Takayuki Osogami, Masaki Ono, Akira Koseki, Michiharu Kudo, Masaki Makino, and Atsushi Suzuki, Maximizing the diversity of exposure in a social network, Cigdem Aslay, Antonis Matakos, Esther Galbrun, and Aristides Gionis, Clustering on Sparse Data in Non-Overlapping Feature Space with Applications to Cancer Subtyping, Tianyu Kang, Kourosh Zarringhalam, Marieke Kuijjer, John Quackenbush, and Wei Ding, Semi-Supervised Community Detection Using Structure and Size, Arjun Bakshi, Srinivasan Parthasarathy, and Kannan Srinivasan, Differentially Private Prescriptive Analytics, Haripriya Harikumar, Santu Rana, Sunil Gupta, Thin Nguyen, Ramachandra Kaimal, and Svetha Venkatesh, Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, and Chengqi Zhang, Interpretable Word Embeddings For Medical Domain, Kishlay Jha, Yaqing Wang, Guangxu Xun, and Aidong Zhang, Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach, Xiaoying Ren, Linli Xu, Tianxiang Zhao, Chen Zhu, Junliang Guo, and Enhong Chen, Neural Sentence-level Sentiment Classification with Heterogeneous Supervision, Zhigang Yuan, Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, and Xing Xie, Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization, Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit, Weitong Chen, Sen Wang, Guodong Long, Lina Yao, Quan Zheng Sheng, and Xue Li, Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation, Yun He, Haochen Chen, Ziwei Zhu, and James Caverlee, A Knowledge-Enhanced Deep Recommendation Framework Incorporating GAN-based Models, Deqing Yang, Zikai Guo, Ziyi Wang, Junyang Jiang, Yanghua Xiao, and Wei Wang, Fast Tucker Factorization for Large-Scale Tensor Completion, Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing, Sein Minn, Yi Yu, Michel Desmarais, Feida Zhu, and Jill-Jenn Vie, Transfer Hawkes Processes with Content Information, Estimating Latent Relative Labeling Importances for Multi-Label Learning, Doc2Cube: Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding, Fangbo Tao, Chao Zhang, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, and Jiawei Han, Adaptive Affinity Learning for Accurate Community Detection, Fanghua Ye, Shenghui Li, Zhiwei Lin, Chuan Chen, and Zibin Zheng, Graph Pattern Mining and Learning through User-defined Relations.
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