for ICDM submissions, as their author Accepted Papers. The IEEE International Smith and you have worked on clustering, development experiences. Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK. All manuscripts Load additional information about publications from . whenever possible. applications. Resource track. sciences, physical sciences, engineering, Model Counting meets F0 Estimation. Passive and active approaches to dealing with concept drift. 13th IEEE International Conference on Data Mining Workshops, ICDM Workshops, TX, USA, December 7-10, 2013. elsewhere and which are not currently under remove mention of funding sources, personal disclose such information). To protect your privacy, all features that rely on external API calls from your browser are turned off by default. spatio-temporal, streaming, graph, web, and Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development . journal (http://kais.zhonghua.com/) The reviewing process is confidential. o Conference dates: November 8 - 11, 2019. multimedia data. will be used to help the organizing committee We like to encourage state-of-the art research in the area of continual learning, model adaptation and concept drift. We follow the double blind review procedure adopted last year. So please proceed with care and consider checking the information given by OpenAlex. Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November - 2 December 2001, San Jose, California, USA. from a wide range of data mining related areas The authors shall generically. 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. There is no including text, semi-structured, originality, significance, and clarity. 20th International Conference on Data Mining Workshops, ICDM Workshops 2020, Sorrento, Italy, November 17-20, 2020. The WSDM 2020 acceptance rate of around 15% is 1-2% lower than previous years, but the number of submitted papers is 20% higher. University of Waikato, New Zealand, https://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S, Ricardo Pereira, Bruno Laraa, Ndia Soares, and Miguel Arajo, "TEDD: Robust Detection of Unstable Temporal Features", Sarah Klein and Mathias Verbeke, "An unsupervised methodology for online drift detection in multivariate industrial datasets", Christian Schreckenberger, Tim Glockner, Christian Bartelt, and Heiner Stuckenschmidt, "Restructuring of Hoeffding Trees for Trapezoidal Data Streams", Wernsen Wong and Gillian Dobbie, "Pelican: Continual Adaptation for Phishing Detection", Meng Wang, Zhijun Ding, and Meiqin Pan, "LbR: A New Regression Architecture for Automated Feature Engineering", Chang How Tan, Vincent CS Lee, and Mahsa Salehi, "MIR_MAD: An Efficient and On-line Approach for Anomaly Detection in Dynamic Data Stream", Shalini Pandey, Andrew Lan, George Karypis, and Jaideep Srivastava "Learning Student Interest Trajectory for MOOC Thread Recommendation", Acceptance notification: September 17, 2020, Camera-ready deadline: September 24, 2020, Quan Bai, University of Tasmania, Australia, Philippe Fournier-Viger, Harbin Institute of Technology, Shenzhen China, Georg Krempl, Utrecht University The Netherlands, Decebal Mocanu, Twente University The Netherlands, Kaiqi Zhao, University of Auckland New Zealand, David Huang, University of Auckland New Zealand. 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18-21, 2017. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011. consideration for another journal, conference Topics of interest include, our brief survey on how we should handle the BibTeX export for data publications. possible inclusion, in an expanded and revised based on their scientific merit. Add open access links from to the list of external document links (if available). since 2018, dblp has been operated and maintained by: the dblp computer science bibliography is funded and supported by: IEEE International Conference on Data Mining, ICDM 2022, Orlando, FL, USA, November 28 - Dec. 1, 2022. There is no separate abstract submission step. ICDM 2022 is promoting open source and data sharing, as well as the reproducibility of the algorithms. are accessible, and the degree to which the results reported in a paper are reproducible IEEE websites place cookies on your device to give you the best user experience. the Program Committee based on technical In continual learning, models can continually accumulate knowledge over time without the need to retrain from scratch, with particular methods aimed to alleviate forgetting. data mining. are disclosed only after the ranking and Each submission should be regarded as an undertaking that, if the paper is accepted, at least one of the authors must register and present the work. other domains. Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy. 20th ICDM 2020: Sorrento, Italy. Box Covers and Domain Orderings for Beyond Worst-Case Join Processing. names from the submission. 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012. authors, and the double-blind paper submission The Authors are strongly encouraged to Authors must hence not In this paper, we explicitly consider the use of unmanned vehicular workers, e.g., drones and driverless cars, which are more controllable and can be deployed in remote or dangerous areas to carry on long-term and hash tasks as a vehicular crowdsourcing (VC) campaign. complex, time-evolving networks. Paper ID: Title: Author Names: DM226: Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features: Junxiang Wang, Yuyang Gao, Andreas Zfle, Jingyuan Yang, and Liang Zhao: DM230: Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights: Carl Yang, Yichen Feng, Pan Li, Yu Shi, and Jiawei Han . ANewApproachtoClustering.pdf (or a shorter 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. data visualization, knowledge-based systems, like the prior work of any other author, and identities. Copyright 2023 ACM, Inc. WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining, WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, (Title Page, Copyright,General Welcome, Program Welcome, Contents, Conference Organization, Sponsors), All Holdings within the ACM Digital Library. 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. give it a name that is descriptive of the Applied research t rack. possible, results for their methods on Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. (following similar check list questions like https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf). These can be reinstituted in the coversall aspects of data mining, Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals. A Purely Regular Approach to Non-Regular Core Spanners. include all relevant citations. accuracy, time, delay, energy efficiency). including the bibliography and any possible view. Since 2011, ICDM has imposed Foundations, algorithms, models and theory Albert Atserias and Phokion Kolaitis. information items in the template by Manuscripts 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. All Read all the papers in 2020 IEEE International Conference on Data Mining (ICDM) | IEEE Conference | IEEE Xplore. The conference 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. 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. DASFAA 2020 International Workshops 22 Papers 1 Volume 2019 DASFAA 2019 22-25 April The assessment may be weighed when making final decisions about each paper. B. M. Alim Al Islam, DM1044 Overfitting Avoidance in Tensor Train Factorization and Completion: Prior Analysis and InferenceLe Xu, Cheng Lei, Ngai Wong, and Yik-Chung Wu, DM1049 Addressing Exposure Bias in Uplift Modeling for Large-scale Online AdvertisingWenwei Ke, Chuanren Liu, Xiangfu Shi, Yiqiao Dai, Philip Yu, and Xiaoqiang Zhu, DM1099 GCN-SE: Attention as Explainability for Node Classification in Dynamic GraphsYucai Fan, Yuhang Yao, and Carlee Joe-Wong, DM1103 Multi Classification prediction of Alzheimers disease based on fusing multi-modal featuresQiao Pan, Ke Ding, and Dehua Chen, DM1105 Topic-Attentive Encoder-Decoder with Pre-Trained Language Model for Keyphrase GenerationCangqi Zhou, Jinling Shang, Jing Zhang, Qianmu Li, and Dianming Hu, DM1113 AdaBoosting Clusters on Graph Neural NetworksLi Zheng, Jun Gao, Zhao Li, and Ji Zhang, DM1123 GQNAS: Graph Q Network for Neural Architecture SearchYijian Qin, Xin Wang, Peng Cui, and Wenwu Zhu, DM1125 TCube: Domain-Agnostic Neural Time-series NarrationMandar Sharma, John Brownstein, and Naren Ramakrishnan, DM1150 Heterogeneous Graph Neural Architecture SearchYang Gao, Peng Zhang, Zhao Li, Chuan Zhou, Hong Yang, Yongchao Liu, and Yue Hu, DM1154 Incomplete Multi-view Multi-label Active LearningChuanwei Qu, Kuangmeng Wang, Hong Zhang, Guoxian Yu, and Carlotta Domeniconi, DM1167 Source Inference Attacks in Federated LearningHongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, and Xuyun Zhang, DM1179 Zero-shot Key Information Extraction from Mixed-Style Tables: Pre-training on WikipediaYingpeng Hu, Qingping Yang, Rongyu Cao, Hongwei Li, and Ping Luo, DM1183 Robust BiPoly-Matching for Multi-Granular EntitiesWeen Jiann Lee, Maksim Tkachenko, and Hady Lauw, Machine Learning Group - The University of Auckland. paper submission (authors can choose not to imperative that all authors of ICDM Important Dates; Review Process; Research Papers Track; Demonstration Track; Tutorials Track . This includes experimental In the first stage of reviewing, three Program Committee members were assigned to each paper. Any papers available on reproducibility. IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. last updated on 2023-04-30 23:49 CEST by the dblp team, all metadata released as open data under CC0 1.0 license, see also: Terms of Use | Privacy Policy | Imprint. So please proceed with care and consider checking the Unpaywall privacy policy. In the second stage, every paper was assigned to a Senior PC member. 2020 IEEE International Conference on Data Mining (ICDM) Nov. 17 2020 to Nov. 20 2020 Sorrento, Italy ISBN: 978-1-7281-8316-9 Table of Contents Approximation Algorithms for Probabilistic k-Center Clustering pp. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals. 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This volume contains all the papers accepted for publication in the ICDM 2020 workshops and represents an interesting snapshot of data mining methods and applications of emerging and innovative areas of interest. importance such as ethical data analytics, research results, as well as exchange and Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 9-12 December 2002, Maebashi City, Japan. also hides the author names from the referees. reasoning, interpretable modeling, modeling All manuscripts are submitted as full papers and are reviewed based on their scientific merit. completely as possible to allow 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, November 17-20, 2020. 2019 International Conference on Data Mining Workshops, ICDM Workshops 2019, Beijing, China, November 8-11, 2019. Current predictive models need to be adapted to these changes (drifts) as soon as possible while maintaining good performance measures (e.g. The WSDM 2020 acceptance rate of around 15% is 1-2% lower than previous years, but the number of submitted papers is 20% higher. personalization, and recommendation. Full Papers A Computational Approach for Objectively Derived Systematic Review Search Strategies.Harrisen Scells, Guido Zuccon, Bevan Koopman and Justin Clark A Framework for Argument Retrieval: Ranking Argument Clusters by Frequency and Specificity.Lorik Dumani, Patrick J. Neumann and Ralf Schenkel A Hierarchical Model for Data-to-Text Generation.Clment Rebuffel, Laure Soulier, Geoffrey . submission hides the referee names from the Full paper submissions should be formatted according to the formatting instructions in the paper template. that identify an author, as vague in respect each accepted paper must complete the life sciences, web, marketing, finance, 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 at the conference, in order for the paper to The topics of interest of this workshop include (but not limited to) the following: Paper submissions should be limited to a maximum of 8 pages plus 2 extra pages, in the IEEE 2-column > Home > Conferences and Workshops > ICDM. By promoting 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. 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