Accept papers: link

anomaly detection (anomaly, outlier, out-of-distribution, one-class, Malware detection, Fraud Detection, Fake News Detection…)

• ELITE : Robust Deep Anomaly Detection with Meta Gradient

  Authors: Huayi Zhang (WPI); Lei Cao (MIT)$^{\star}$; Peter VanNostrand (WPI); Samuel Madden (MIT); Elke A Rundensteiner (WPI)

• Joint Optimization of Known and Unknown Anomaly Detection

  Authors: Guansong Pang (University of Adelaide)$^{\star}$; Anton van den Hengel (University of Adelaide); Chunhua Shen (University of Adelaide); Longbing Cao (University of Technology Sydney)

• Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

  Authors: Zhiwei Wang (Michigan State University)$^{\star}$; Zhengzhang Chen (NEC Laboratories America, Inc.); Jingchao Ni ( NEC Laboratories America); Hui Liu (Michigan State University); Haifeng Chen (NEC Labs); Jiliang Tang (Michigan State University)

• Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding

  Authors: Zhihan Li (Tsinghua University)$^{\star}$; Youjian Zhao (Tsinghua University); Jiaqi Han (Tsinghua University); Ya Su (Tsinghua University); Rui Jiao (Tsinghua University); Xidao Wen (Tsinghua University); Dan Pei (Tsinghua University)

• Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization

  Authors: Ahmed Abdulaal (eBay)$^{\star}$; Zhuanghua Liu (eBay); Tomer Lancewicki (EBay)

• Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering

  Authors: Cheng Feng (Siemens)$^{\star}$; Pengwei Tian (Siemens)

• Deep Clustering-based Fair Outlier Detection

  Authors: Hanyu Song (Brandeis University)$^{\star}$; Peizhao Li (Brandeis University); Hongfu Liu (Brandeis University)

• Fast One-class Classification using Class Boundary-preserving Random Projections

  Authors: Arindam Bhattacharya (IIT DELHI)$^{\star}$; Sumanth Varambally (IIT Delhi); Amitabha Bagchi (IIT Delhi); Srikanta Bedathur (IIT Delhi)

• Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection

  Authors: Yujie Fan (Case Western Reserve University); Mingxuan Ju (Case Western Reserve University); Shifu Hou (Case Western Reserve University); Yanfang Ye (Case Western Reserve University)$^{\star}$; Wenqiang Wan (Tencent Security Lab); Kui Wang (Tencent Security Lab); Yinming Mei (Tencent Security Lab); Qi Xiong (Tencent Security Lab)

• Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach

  Authors: Zhao Li (Alibaba Group); Haishuai Wang (Fairfield University，Department of Computer Science and Engineering); Peng Zhang (Guangzhou University)$^{\star}$; Pengrui Hui ( Alibaba Group); Jiaming Huang (Alibaba Group); Jian Liao (Alibaba Group); Ji Zhang (The University of Southern Queensland); Jiajun Bu (Zhejiang University)

• Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection

  Authors: Can Liu (Alibaba Group)$^{\star}$; Li Sun (Alibaba Group); Xiang Ao (Institute of Computing Technology, CAS); Jinghua Feng (Ailbaba Group ); Qing He (Institute of Computing Technology, Chinese Academy of Sciences); Hao Yang (Alibaba Group)

• Multi-modal Emergent Fake News Detection via Meta Neural Process Networks

  Authors: Yaqing Wang (Purdue University)$^{\star}$; Fenglong Ma (Pennsylvania State University); Haoyu Wang (SUNY Buffalo); Kishlay Jha (University of Virginia); Jing Gao (University at Buffalo)

• Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies

  Authors: Lev Faivishevsky (Intel)$^{\star}$; Adi Szeskin (Intel); Ashwin k Muppalla (Intel); Ravid Ziv (Intel); Ronen Laperdon (Intel); Benjamin Melloul (intel); Tahi Hollander (Intel); Tom Hope (Intel); Amitai Armon (Intel)

Time series

• Apriori Convolutions for Highly Efficient and Accurate Time Series Classification

  Authors: Angus Dempster (Monash University)$^{\star}$; Daniel F Schmidt (Monash University); Geoffrey I Webb (Monash)

• Fast and Accurate Partial Fourier Transform for Time Series Data

  Authors: Yong-chan Park (Seoul National University)$^{\star}$; Jun-gi Jang (Seoul National University); U Kang (Seoul National University)

• Representation Learning of Multivariate Time Series using a Transformer Framework

  Authors: George Zerveas (Brown University)$^{\star}$; Srideepika Jayaraman (IBM); Dhaval Patel (IBM TJ Watson Research Center); Anuradha Bhamidipaty (IBM Watson Research Center); Carsten Eickhoff (Brown University)

• ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting

  Authors: Jinliang Deng (University of Technology Sydney); Xiusi Chen (University of California, Los Angeles); Renhe Jiang (The University of Tokyo); Xuan Song (Southern University of Science and Technology); Ivor Tsang (University of Technology Sydney)$^{\star}$

• Statistical models coupling allows for complex localmultivariate time series analysis

  Authors: Veronica Tozzo (Massachusets General Hospital - Harvard Medical School)$^{\star}$; Federico Ciech (University of Genoa); Davide Garbarino (University of Genoa); Alessandro Verri (University of Genova, Italy)

• Causal and Interpretable Rules for Time Series Analysis

  Authors: Amin Dhaou (Total)$^{\star}$; Josselin Garnier (École Polytechnique); Antoine Bertoncello (Total); Erwann LE PENNEC (Polytechnique)

Graph Representation Learning

• Are we really making much progress? Revisiting, benchmarking and refining the Heterogeneous Graph Neural Networks

  Authors: Qingsong Lv (Tsinghua University); Ming Ding (Tsinghua University); Qiang Liu (Institute of Information Engineering, Chinese Academy of Sciences); Yuxiang Chen (Tsinghua University); Wenzheng Feng (Tsinghua University); Siming He (University of Pennsylvania); Chang Zhou (Alibaba Group); Jian-guo Jiang (Institute of Information Engineering , Chinese Academy of Sciences); Yuxiao Dong (Facebook AI); Jie Tang (Tsinghua University)$^{\star}$

• Attentive Heterogeneous Graph Embedding for Job Mobility Prediction

  Authors: Le Zhang (University of Science and Technology of China)$^{\star}$; Ding Zhou (University of Science and Technology of China); Hengshu Zhu (Baidu Talent Intelligence Center, Baidu Inc.); Tong Xu (University of Science and Technology of China); Rui Zha ( University of Science and Technology of China); Enhong Chen (University of Science and Technology of China); Hui Xiong (Rutgers University)

• DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks

  Authors: Yuhui Ding (The Hong Kong University of Science and Technology)$^{\star}$; Quanming Yao (4Paradigm); Huan Zhao (4Paradigm Inc.); Tong Zhang (Hong Kong University of Science and Technology)

• HGK-GNN: Heterogeneous Graph Kernel based Graph Neural Networks

  Authors: Qingqing Long (Peking University)$^{\star}$; Lingjun Xu (Peking University); Zheng Fang (pku); Guojie Song (Peking University)

• Pre-training on Large-Scale Heterogeneous Graph

  Authors: Xunqiang Jiang (Beijing University of Posts and Telecommunications)$^{\star}$; Tianrui Jia (Beijing University of Posts and Telecommunications); Chuan Shi (Beijing University of Posts and Telecommunications); Yuan Fang (Singapore Management University); Zhe Lin (Peng Cheng Laboratory); Hui Wang (Peng Cheng Laboratory)

• Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups

  Authors: SHENGMING ZHANG (Rutgers University)$^{\star}$; Hao Zhong (ESCP Business School); Zixuan Yuan (Rutgers University); Hui Xiong (the State University of New Jersey)

• Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

  Authors: Xiao Wang (Beijing University of Posts and Telecommunications); Nian Liu (Beijing University of Posts and Telecommunications)$^{\star}$; Hui Han (Beijing University of Posts and Telecommunications); Chuan Shi (Beijing University of Posts and Telecommunications)

• Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection

  Authors: Yujie Fan (Case Western Reserve University); Mingxuan Ju (Case Western Reserve University); Shifu Hou (Case Western Reserve University); Yanfang Ye (Case Western Reserve University)$^{\star}$; Wenqiang Wan (Tencent Security Lab); Kui Wang (Tencent Security Lab); Yinming Mei (Tencent Security Lab); Qi Xiong (Tencent Security Lab)

• HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps

  Authors: Jizhou Huang (Baidu)$^{\star}$; Haifeng Wang (Baidu); Yibo Sun (Baidu); Miao Fan (Baidu); Zhengjie Huang (Baidu); Chunyuan Yuan (Baidu); Yawen Li (BUPT)

sequence

• PETGEN: Personalized Text Generation Attack on Deep User Sequence Classification ModelsAuthors: Bing He (Georgia Institute of Technology)$^{\star}$; Dr.Mustaque Ahamad (Georgia Institute of Technology); Srijan Kumar (Georgia Institute of Technology)
• TimeSHAP: Explaining Recurrent Models through Sequence PerturbationsAuthors: Joao Bento (Feedzai); Pedro Saleiro (Feedzai)$^{\star}$; André F. Cruz (Feedzai); Mario Figueiredo (University of Lisbon); Pedro Bizarro (Feedzai)

causal analysis

• Causal models for Real Time Bidding with repeated user interactions

  Authors: Martin Bompaire (Criteo)$^{\star}$; Benjamin Heymann (Criteo); Alexandre Gilotte (Criteo)

• DARING: Differentiable Causal Discovery with Residual Independence

  Authors: Yue He (Tsinghua University)$^{\star}$; Peng Cui (Tsinghua University); Zheyan Shen (Tsinghua University); Renzhe Xu (Tsinghua University); Furui Liu (Huawei Noah’s Ark Lab); Yong Jiang (Tsinghua University)

• MPCSL - A Modular Pipeline for Causal Structure Learning

  Authors: Johannes Huegle (Hasso Plattner Institute)$^{\star}$; Christopher Hagedorn (Hasso Plattner Institute); Michael Perscheid (Hasso Plattner Institute); Hasso Plattner (Hasso Plattner Institute)

clustering

About distribution

interpretable [Understanding, explanation, Attribution …]

missing value & irregularly sampled time series [Incomplete, imputation, …]