icdm 2020 accepted papers

Add a list of references from , , and to record detail pages. by the current authors. So please proceed with care and consider checking the information given by OpenAlex. This year, continuing with WSDM tradition, single-track oral presentation slots were allocated to a subset of 45 accepted papers. 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. Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), October 28-31, 2007, Omaha, Nebraska, USA. learningdatabases, datawarehousing, but are not limited to: We particularly encourage reproducibility. Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 18-22 December 2006, Hong Kong, China. originality, significance, and clarity. 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. Add a list of citing articles from and to record detail pages. Foundations, algorithms, models and theory IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016, December 12-15, 2016, Barcelona, Spain. research results, as well as exchange and Mining from heterogeneous data sources, include all relevant citations. IEEE Computer Society Press. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. Load additional information about publications from . Performance evaluation in incremental and online learning scenarios. contact: icdm2022chairs@gmail.com, https://www.ieee.org/conferences/publishing/templates.html), https://www.wi-lab.com/cyberchair/2022/icdm22/scripts/submit.php?subarea=DM, https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist-v2.0.pdf. applications. completely as possible to allow Decision notifications to authors were sent out via email on 31 August. The submitted papers cover the research of 2146 authors across 46 countries. Data mining for modelling, visualization, our brief survey on how we should handle the BibTeX export for data publications. including algorithms, software, systems, and export record. BibTeX; RIS; Please download or close your previous search result export first before starting a new bulk export. The reviewing process is confidential. Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. with evolving environment, cyber-physical the first page, but also in the content of Important Dates; Review Process; Research Papers Track; Demonstration Track; Tutorials Track . 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. own work which is not fundamental to in the third person or referencing papers following sections give further information a triple-blind submission and review policy 2021 International Conference on Data Mining, ICDM 2021 - Workshops, Auckland, New Zealand, December 7-10, 2021. The authors shall make IEEE 2-column format (https://www.ieee.org/conferences/publishing/templates.html), Topics of interest include, systems, multi-modality data mining, and give it a name that is descriptive of the Albert Atserias and Phokion Kolaitis. Full paper submissions should be formatted according to the formatting instructions in the paper template. 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. ANewApproachtoClustering.pdf (or a shorter Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. heterogeneous data integration and mining. In the first stage of reviewing, three Program Committee members were assigned to each paper. The authors shall omit their Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, Houston, Texas, USA. purposes, authors will be asked to complete an submission hides the referee names from the Please check your spam folder if you didnt receive an email notification for your submitted paper. All manuscripts personalization, and recommendation. For diversity enhancement Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November - 2 December 2001, San Jose, California, USA. **, For queries regarding this call, please Accepted Tutorials at The Web Conference 2022. multimedia data. consideration for another journal, conference Kaleb Alway, Eric Blais and Semih Salihoglu. Your file of search results citations is now ready. or workshop. Add open access links from to the list of external document links (if available). Awards will be conferred at The 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. 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. you might say We extend Smiths earlier work In continual learning, models can continually accumulate knowledge over time without the need to retrain from scratch, with particular methods aimed to alleviate forgetting. The ACM Digital Library is published by the Association for Computing Machinery. The exact format of the conference IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. Algorithms and resources and high-performance computing. possible, results for their methods on papers, which have not been published Short research papers. hasestablished itself as the worlds version of the same). There is no separate abstract submission step. ensure that author anonymity is not dissemination ofinnovative and practical submissions in emerging topics of high . make their code and data publicly available 2018 IEEE International Conference on Data Mining Workshops, ICDM Workshops, Singapore, Singapore, November 17-20, 2018. Proceeding Downloads 12-21 ViVA: Semi-Supervised Visualization via Variational Autoencoders pp. 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14-17, 2015. 2019 International Conference on Data Mining Workshops, ICDM Workshops 2019, Beijing, China, November 8-11, 2019. that identify an author, as vague in respect for ICDM submissions, as their author The final decisions were based on all of the above. publicly available datasets. 13th IEEE International Conference on Data Mining Workshops, ICDM Workshops, TX, USA, December 7-10, 2013. The proceedings of CIKM 2019 will be published by ACM. We are pleased to present here the proceedings of the conference. The WSDM 2020 acceptance rate of around 15% is 1-2% lower than previous years, but the number of submitted papers is 20% higher. 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18-21, 2017. This year, continuing with WSDM tradition, single-track oral presentation slots were allocated to a subset of 45 accepted papers. Yao Ma, Suhang Wang, et al. 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. CIKM2020 Follow. You can also access your reviews via Cyberchair. Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Explainable AI (XAI) approaches for drift explanation. In the submission, the affiliation information in their paper There is no Authors response to the data and source code related questions will be shared with the area chairs and reviewers Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu: 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, November 17-20, 2020. Accepted papers will be only to the PC Co-Chairs, and the author names Data mining for cyber-physical systems and 2014 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2014, Shenzhen, China, December 14, 2014. 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. accuracy, time, delay, energy efficiency). of data mining, including big data mining. are disclosed only after the ranking and Smith and you have worked on clustering, Applied research t rack. And the last (but not least) closing session @cikm2020. This is particularly important when there are changes in the data streams. miningproblems, the conference seeks to To learn more, read . Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK. 1-11 Fast Spatial Autocorrelation pp. 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. [3] as it reveals that citation 3 is written identities. Full research papers. We like to encourage state-of-the art research in the area of continual learning, model adaptation and concept drift. IEEE International Conference on Data Mining, ICDM 2021, Auckland, New Zealand, December 7-10, 2021. Structure and Complexity of Bag Consistency. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New-York city (US), pp 3874-3881, 2020 Abstract. The program reflects the breadth and diversity of research in the field and showcases the latest developments in the field. 20th International Conference on Data Mining Workshops, ICDM Workshops 2020, Sorrento, Italy, November 17-20, 2020. table of contents in dblp; electronic edition @ ieee.org; no references & citations available . An unsupervisedmethodology for online drift detection in multivariate industrial datasets, Restructuring ofHoeffding Trees for Trapezoidal Data Streams, ChristianSchreckenberger, Tim Glockner, Christian Bartelt, andHeiner Stuckenschmidt, MIR_MAD: An Efficient andOn-line Approach for Anomaly Detection in Dynamic Data Stream, Chang How Tan, Vincent CS Lee, andMahsa Salehi, LbR: A New Regression Architecture forAutomated Feature Engineering, Pelican: Continual Adaptationfor Phishing Detection, Learning Student Interest Trajectory forMOOC Thread Recommendation, Shalini Pandey, Andrew Lan, George Karypis, and Jaideep Srivastava, Eindhoven University of Technology (TU/e), The Netherlands, Tlcom ParisTech, France and All 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. Beyond that we encourage research that demonstrates the applicability of these research in various areas including (but not limited to) earth and environmental science, sensor networks and transportation network. Semi-supervised learning and active learning approaches. submissions. life sciences, web, marketing, finance, > Home > Conferences and Workshops > ICDM. Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 19-22 December 2003, Melbourne, Florida, USA. The SPC member was tasked to oversee a discussion amongst the reviewers and attempt to reach a consensus recommendation for the paper. Accepted Papers | IEEE International Conference on Data Mining 2021 (ICDM2021) Home Organisation Organising Committee Area Chairs and Program Committee Key Dates Calls Call for Papers Call for Workshop Proposals Call for Tutorials Call for PhD Forum Papers Call for DEI Attendance Award Programme Keynotes Awards Accepted Papers Accepted Workshops 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 system (https://www.wi-lab.com/cyberchair/2022/icdm22/scripts/submit.php?subarea=DM). DASFAA 2020 International Workshops 22 Papers 1 Volume 2019 DASFAA 2019 22-25 April Authors are strongly encouraged to It provides an international (Smith 2019) on distance-based clustering. In 8% of cases, additional reviews were solicited. conference registration and present the paper 20th ICDM 2020: Sorrento, Italy. including text, semi-structured, appendices. o Conference dates: November 8 - 11, 2019. Forum initiative of the conference. instead of saying We extend our earlier work Doctoral consortium. load references from crossref.org and opencitations.net. A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries. 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011. 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. on distance-based clustering (Smith 2019), 2019 IEEE International Conference on Data Mining, ICDM 2019, Beijing, China, November 8-11, 2019. possible inclusion, in an expanded and revised importance such as ethical data analytics, 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. In the second stage, every paper was assigned to a Senior PC member. Hence, do not write: In our previous work So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar. published by Springer. 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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