Accepted paper list: Link

Time Series

• A Geometric Approach to Time Series Chains Improves Robustness

  Authors: Makoto Imamura: Tokai University; Takaaki Nakamura: Mitsubishi Electric Corporation; Eamonn Keogh: University of California - Riverside

• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

  Authors: Zonghan Wu: University of Technology Sydney; Shirui Pan: Monash University; Guodong Long: University of Technology Sydney; Jing Jiang: University of Technology Sydney; Xiaojun Chang: Monash University; Chengqi Zhang: University of Technology Sydney

• Fast R-STL: Efficient and Robust Seasonal-Trend Decompositionfor Time Series with Complex Patterns

  Authors: Qingsong Wen: Alibaba Group U.S.; Zhe Zhang: Alibaba Group U.S.; Yan Li: Alibaba Group U.S.; Liang Sun: Alibaba Group U.S.

• Fitbit for Chickens? Time Series Data Mining Can Increase the Productivity of Poultry Farms

  Authors: Alireza Abdoli: University of California Riverside; Sara Alaee: University of California Riverside; Shima Imani: University of California Riverside; Amy Murillo: University of California Riverside ; Alec Gerry: UC Riverside; Leslie Hickle: FarmSense Inc; Eamonn Keogh: UC Riverside

• USAD : UnSupervised Anomaly Detection on multivariate time series

  Authors: Julien Audibert: Orange EURECOM; Pietro Michiardi: EURECOM; Frédéric Guyard: Orange Labs; Sébastien Marti: Orange; Maria A. Zuluaga: EURECOM

• HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records

  Authors: Junyu Luo: The Pennsylvania State University; Muchao Ye: The Pennsylvania State University; Cao Xiao: IQVIA; Fenglong Ma: The Pennsylvania State University

• Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder

  Authors: Changchang Yin: The Ohio State University; Ruoqi Liu: The Ohio State University; Dongdong Zhang: The Ohio State University; Ping Zhang: The Ohio State University

• Local Motif Clustering on Time-Evolving Graphs

  Authors: Dongqi Fu: University of Illinois at Urbana-Champaign; Dawei Zhou: University of Illinois at Urbana-Champaign; Jingrui He: University of Illinois at Urbana-Champaign

• Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

  Authors: Garrett Wilson: Washington State University; Janardhan Rao Doppa: Washington State University; Diane J. Cook: Washington State University

• Sliding Sketches: A Framework using Time Zones for Data Stream Processing in Sliding Windows

  Authors: Xiangyang Gou: Peking University; Long He: Peking University; Yinda Zhang: Peking University; Ke Wang: Peking University; Xilai Liu: Peking University; Tong Yang: Peking University; Yi Wang: Southern University of Science and Technology; Bin Cui: Peking University

• Attention based multi-modal new product sales time-series forecasting

  Authors: Vijay Ekambaram: IBM Research; Kushagra Manglik: IBM Research; Sumanta Mukherjee: IBM Research; Surya Shravan Kumar Sajja: IBM Research; Satyam Dwivedi: IBM Research; Vikas Raykar: IBM Research

• BusTr: predicting bus travel times from real-time traffic

  Authors: Richard Barnes: UC Berkeley; Senaka Buthpitiya: Google Research; James Cook: N ne; Alex Fabrikant: Google Research; Andrew Tomkins: Google Research; Fangzhou Xu: Google Research

• Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

  Authors: Daheng Wang: University of Notre Dame; Meng Jiang: University of Notre Dame; Munira Syed: University of Notre Dame; Oliver Conway: Conde Nast; Vishal Juneja: Conde Nast; Sriram Subramanian: Conde Nast; Nitesh V. Chawla: University of Notre Dame

• HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival

  Authors: Huiting Hong: AI Labs Didi Chuxing Beijing China ; Yucheng Lin: AI Labs Didi Chuxing Beijing China ; Xiaoqing Yang: AI Labs Didi Chuxing Beijing China ; Zang Li: AI Labs Didi Chuxing Beijing China ; Jieping Ye: AI Labs Didi Chuxing Beijing China ; Kun Fu: AI Labs Didi Chuxing Beijing China ; Zheng Wang: AI Labs Didi Chuxing Beijing China ; Xiaohu Qie: Technology Ecosystem Development Didi Chuxing Beijing China

• Heidegger: Interpretable Temporal Causal Discovery

  Authors: Mehrdad Mansouri: Simon Fraser University; Ali Arab: Simon Fraser University; Zahra Zohrevand: Simon Fraser University; Martin Eser: Simon Fraser University

• LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values

  Authors: Kejing Yin: Hong Kong Baptist University; Ardavan Afshar: Georgia Institute of Technology; Joyce Ho: Emory University; William Cheung: Hong Kong Baptist University; Chao Zhang: Georgia Institute of Technology; Jimeng Sun: University of Illinois Urbana-Champaign

• Predicting Temporal Sets with Deep Neural Networks

  Authors: Le Yu: Beihang University; Leilei Sun: Beihang University; Bowen Du: Beihang University; Chuanren Liu: University of Tennessee; Hui Xiong: Rutgers University; Weifeng Lv: Beihang University

missing value or irregularly sampled time series

• Missing Value Imputation for Mixed Data via Gaussian Copula

  Authors: Yuxuan Zhao: Cornell University; Madeleine Udell: Cornell University

• LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values

  Authors: Kejing Yin: Hong Kong Baptist University; Ardavan Afshar: Georgia Institute of Technology; Joyce Ho: Emory University; William Cheung: Hong Kong Baptist University; Chao Zhang: Georgia Institute of Technology; Jimeng Sun: University of Illinois Urbana-Champaign

Recurrent Neural Network

• Recurrent Halting Chain for Early Multi-label Classification

  Authors: Thomas Hartvigsen: Worcester Polytechnic Institute; Cansu Sen: Worcester Polytechnic Institute; Xiangnan Kong: Worcester Polytechnic Institute; Elke Rundensteiner: Worcester Polytechnic Institute

• Recurrent Networks for Guided Multi-Attention Classification

  Authors: Xin Dai: Worcester Polytechnic Institute; Xiangnan Kong: Worcester Polytechnic Institute; Tian Guo: Worcester Polytechnic Institute; John Lee: Worcester Polytechnic Institute; Xinyue Liu: Worcester Polytechnic Institute; Constance Moore: University of Massachusetts Medical School

• A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario

  Authors: Monidipa Das: Nayang Technological University NTU Singapore ; Mahardhika Pratama: Nanyang Technological University NTU ; Tegoeh Tjahjowidodo: KU Leuven

• A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications

  Authors: Kevin Yancey: Duolingo; Burr Settles: Duolingo

• Hypergraph Convolutional Recurrent Neural Network

  Authors: Jaehyuk Yi: KAIST; Jinkyoo Park: KAIST

Anomaly Detection

• Isolation Distributional Kernel: A new tool for kernel based anomaly detection

  Authors: Kai Ming Ting: Nanjing University; Takashi Washio: Osaka University; Bi-Cun Xu: Nanjing University; Zhi-Hua Zhou: Nanjing University

• USAD : UnSupervised Anomaly Detection on multivariate time series

  Authors: Julien Audibert: Orange EURECOM; Pietro Michiardi: EURECOM; Frédéric Guyard: Orange Labs; Sébastien Marti: Orange; Maria A. Zuluaga: EURECOM

• Generic Outlier Detection in Multi-Armed Bandit

  Authors: Yikun Ban: University of Illinois at Urbana-Champaign; Jingrui He: University of Illinois at Urbana-Champaign

• Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping

  Authors: Susik Yoon: Korea Advanced Institute of Science and Technology; Jae-Gil Lee: Korea Advanced Institute of Science and Technology; Byung Suk Lee: University of Vermont

• Interleaved Sequence RNNs for Fraud Detection 是一个关于银行卡欺诈检测的，数据是多维非均匀采样序列。这个检测问题被转化为一个监督学习问题。

  Authors: Bernardo Branco: Feedzai; Pedro Abreu: QuantumBlack a McKinsey company ; Ana Sofia Gomes: Feedzai; Mariana Almeida: Cleverly; João Tiago Ascensão: Feedzai; Pedro Bizarro: Feedzai

• Grounding Visual Concepts for Multimedia Event Detection and Multimedia Event Captioning in Zero-shot Setting

  Authors: Zhihui Li: University of New South Wales; Xiaojun Chang: Monash University; Lina Yao: University of New South Wales; Shirui Pan: Monash University; Zongyuan Ge: Monash University; Huaxiang Zhang: Shandong Normal University

• Multi-class Data Description for Out-of-distribution Detection

  Authors: Dongha Lee: Pohang University of Science and Technology; Sehun Yu: Pohang University of Science and Technology; Hwanjo Yu: Pohang University of Science and Technology

• CrowdQuake: A Networked System of Low-Cost Sensors for Earthquake Detection via Deep Learning

  Authors: Xin Huang: Florida Institute of Technology; Jangsoo Lee: Kyungpook National University; Young-Woo Kwon: Kyungpook National University; Chul-Ho Lee: Florida Institute of Technology

• DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection 是一个关于交易异常检测的，数据集是交易记录，基于一个Tree-aware的结构进行特征提取

  Authors: Sundong Kim: Institute for Basic Science; Yu-Che Tsai: National Cheng Kung University; Karandeep Singh: Institute for Basic Science; Yeonsoo Choi: World Customs Organization; Etim Ibok: Nigeria Customs Service; Cheng-Te Li: National Cheng Kung University; Meeyoung Cha: Institute for Basic Science

Change point detection

• A Non-Iterative Quantile Change Detection Method in Mixture Model with Heavy-Tailed Components

  Authors: Yuantong Li: Purdue University; Qi Ma: North Carolina State University; Sujit Ghosh: North Carolina State University

• Laplacian Change Point Detection for Dynamic Graphs

  Authors: Shenyang Huang: McGill University, Quebec Institute for Artificial Intelligence (Mila); Yasmeen Hitti: McGill University, Quebec Institute for Artificial Intelligence (Mila); Guillaume Rabusseau: University of Montreal, Quebec Institute for Artificial Intelligence (Mila); Reihaneh Rabbany: McGill University, Quebec Institute for Artificial Intelligence (Mila)

Sequence

• Interleaved Sequence RNNs for Fraud Detection

  Authors: Bernardo Branco: Feedzai; Pedro Abreu: QuantumBlack a McKinsey company ; Ana Sofia Gomes: Feedzai; Mariana Almeida: Cleverly; João Tiago Ascensão: Feedzai; Pedro Bizarro: Feedzai

Interpretable

• Adversarial Infidelity Learning for Model Interpretation

  Authors: Jian Liang: Cloud and Smart Industries Group, Tencent, China; Bing Bai: Cloud and Smart Industries Group, Tencent, China; Yuren Cao: Cloud and Smart Industries Group, Tencent, China; Kun Bai: Cloud and Smart Industries Group, Tencent, China; Fei Wang: Cornell University

• Heidegger: Interpretable Temporal Causal Discovery

  Authors: Mehrdad Mansouri: Simon Fraser University; Ali Arab: Simon Fraser University; Zahra Zohrevand: Simon Fraser University; Martin Eser: Simon Fraser University

• INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare

  Authors: Xianli Zhang: Xi’an Jiaotong University; Buyue Qian: Xi’an Jiaotong University; Shilei Cao: Tencent Jarvis Lab; Yang Li: Xi’an Jiaotong University; Hang Chen: Xi’an Jiaotong University; Yefeng Zheng: Tencent Jarvis Lab; Ian Davidson: University of California - Davis

• Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense

  Authors: Jingyuan Wang: Beihang University; Yufan Wu: Beihang University; Mingxuan Li: Beihang University; Xin Lin: Beihang University; Junjie Wu: Beihang University; Chao Li: Beihang University

• Malicious Attacks against Deep Reinforcement Learning Interpretations

  Authors: Mengdi Huai: University of Virginia; Jianhui Sun: University of Virginia; Renqin Cai: University of Virginia; Liuyi Yao: University of New York at Buffalo; Aidong Zhang: University of Virginia

• GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model

  Authors: Thai Le: The Pennsylvania State University; Suhang Wang: The Pennsylvania State University; Dongwon Lee: The Pennsylvania State University

• xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis

  Authors: Menghai Pan: Worcester Polytechnic Institute; Weixiao Huang: Worcester Polytechnic Institute; Yanhua Li: Worcester Polytechnic Institute (WPI); Xun Zhou: University of Iowa; Jun Luo: Lenovo Group Limited

• Explainable classification of brain networks via contrast subgraphs

  Authors: Tommaso Lanciano: La Sapienza University of Rome; Francesco Bonchi: Fondazione ISI; Aristides Gionis: KTH Royal Institute of Technology

Autoencoder

• High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

  Authors: Nicholas Gao: NASA Ames Research Center; Max Wilson: NASA Ames Research Center; Thomas Vandal: NASA Ames Research Center; Walter Vinci: NASA Ames Research Center; Ramakrishna Nemani: NASA Ames Research Center; Eleanor Rieffel: NASA Ames Research Center

LSTM

• Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades

  Authors: Francesco Ducci: ETH Zurich; Mathias Kraus: ETH Zurich; Stefan Feuerriegel: ETH Zurich

Data augmentation

• NodeAug: Semi-Supervised Node Classification with Data Augmentation

  Authors: Yiwei Wang: National University of Singapore; Wei Wang: National University of Singapore; Yuxuan Liang: National University of Singapore; Yujun Cai: Nanyang Technological University; Juncheng Liu: National University of Singapore; Bryan Hooi: National University of Singapore