Other Outstanding Papers

2019/12/31 00:00:00 2019/12/31 00:00:00 paper list

KDD2019

  • Robust High Dimensional Stream Classification with Novel Class Detection

    Zhuoyi WANG (University of Texas at Dallas)*; Zelun Kong (University of Texas at Dallas); Swarup Chandra (University of Texas at Dallas); Hemeng Tao (The University of Texas at Dallas); Latifur Khan (The university of Texas at Dallas)

  • TARDIS: Distributed Indexing Framework for Big Time Series Data

    liang zhang (WPI)*; Noura S Alghamdi (WPI); Mohamed Y. Eltabakh (Worcester Polytechnic Institute); Elke Rundensteiner (WPI)

  • DBSVEC: Density-Based Clustering Using Support Vector Expansion

    Zhen Zohn Wang (Tsinghua University)*; Rui Zhang (” University of Melbourne, Australia”); Jianzhong Qi (The University of Melbourne); Bo Yuan (Tsinghua University)

  • Adaptive Wavelet Clustering for Highly Noisy Data

    Zengjian Chen (Huazhong University of Science and Technology); Jiayi Liu (University of Massachusetts Amherst)*; Yihe Deng (University of California, Los Angeles); Kun He (Huazhong University of Science and Technology); John E Hopcroft (Cornell University)

  • DBSCAN-MS: Distributed Density-Based Clustering in Metric Spaces

    Keyu Yang (Zhejiang University); Yunjun Gao (” Zhejiang University, China”)*; Rui Ma (Zhejiang University); Lu Chen (Aalborg University, Denmark); Sai Wu (Zhejiang Univ); Gang Chen (Zhejiang University)

  • 已读 Deep Anomaly Detection with Deviation Networks

    Authors: Guansong Pang (The University of Adelaide);Chunhua Shen (The University of Adelaide);Anton van den Hengel (The University of Adelaide);

  • dEFEND: Explainable Fake News Detection 没有借鉴性,需要借助新闻的评论来识别

    Authors: Kai Shu (Arizona State University);Limeng Cui (The Pennsylvania State University);Suhang Wang (The Pennsylvania State University);Dongwon Lee (Penn State Univeristy);Huan Liu (Arizona State University);

  • Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach

    Authors: Minji Yoon (Carnegie Mellon University);Bryan Hooi (Carnegie Mellon University);Kijung Shin (Carnegie Mellon University);Christos Faloutsos (Carnegie Mellon University);

  • Sequential Anomaly Detection using Inverse Reinforcement Learning 有很多的关于强化学习的内容,不太适合

    Authors: Min-Hwan Oh (Columbia University);Garud Iyengar (Columbia University);

  • 已读 Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

    Authors: Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun and Dan Pei

  • Time-Series Anomaly Detection Service at Microsoft

    Authors: Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Tony Xing, Xiaoyu Kou, Mao Yang and Jie Tong

  • Sets2Sets: Learning from Sequential Sets with Neural Networks

    Authors: Haoji Hu (University of Minnesota);Xiangnan He (University of Science and Technology of China);

  • Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training?

这个看起来像是一个有监督的过程,用分类任务做实验。大意是生成一些扰动,让这些扰动附加在原始序列上,然后教导网络去识别出这些扰动。网络挺复杂的

Authors: Xiaowei Jia (University of Minnesota);Sheng Li (University of Georgia);Handong Zhao (Adobe);Sungchul Kim (Adobe);Vipin Kumar (University of Minnesota);
  • HOLMES: Real-Time APT Detection through Correlation of Suspicious Information Flows 看一下,怎么做实时的

  • From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms

    Thanh Tam Nguyen (Ecole Polytechnique Federale de Lausanne), Matthias Weidlich (Humboldt-Universität zu Berlin), Bolong Zheng (Huazhong University of Science and Technology), Hongzhi Yin (The University of Queensland), Nguyen Quoc Viet Hung (Griffith University), and Bela Stantic (Griffith University)

  • Efficient Discovery of Sequence Outlier Patterns 好像是做日志的

    Lei Cao (MIT), Yizhou Yan (Worcester Polytechnic Institute), Samuel Madden (MIT), Elke Rundensteiner (Worcester Polytechnic Institute), and Mathan Gopalsamy (Signify Research, Cambridge, MA USA)

  • NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing

    Susik Yoon (KAIST), Jae-Gil Lee (KAIST), and Byung Suk Lee (University of Vermont)

  • GRAIL: Efficient Time-Series Representation Learning 特复杂,好像还没啥用

    John Paparrizos (University of Chicago) and Michael Franklin (University of Chicago)


ICDE2018

  • Polygraph: A Plug-n-Play Framework to Quantify Anomalies

    Yazeed Alabdulkarim (University of Southern California)
    Marwan Almaymoni (University of Southern California)
    Shahram Ghandeharizadeh (University of Southern California)

  • Efficient Learning Interpretable Shapelets for Accurate Time Series Classification

    Zicheng Fang (Fudan University)
    Peng Wang (Fudan University)
    Wei Wang (Fudan University)

  • Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances

    Rodica Neamtu (Worcester Polytechnic Institute)
    Ramoza Ahsan (Worcester Polytechnic Institute)
    Elke Rundensteiner (Worcester Polytechnic Institute)
    Gabor Sarkozy (Worcester Polytechnic Institute)
    Eamonn Keogh (UC Riverside)
    Hoang Anh Dau (UC Riverside)
    Cuong Nguyen (Worcester Polytechnic Institute)
    Charles Lovering (Worcester Polytechnic Institute)

  • Ensemble Direct Density Ratio Estimation for Multistream Classification

    Swarup Chandra (University of Texas at Dallas)
    Ahsanul Haque (University of Texas at Dallas)
    Hemeng Tao (University of Texas at Dallas)
    Latifur Khan (University of Texas at Dallas)
    Jie Liu (University of Texas at Dallas)
    Charu Aggarwal (IBM Research)


ICDE2016

  • A model-based approach for text clustering with outlier detection. 625-636

  • A new privacy-preserving solution for clustering massively distributed personal times-series. 1370-1373

  • Time-series classification with COTE: The collective of transformation-based ensembles. 1548-1549

  • Fast motif discovery in short sequences. 1158-1169


ICDM2015

  • Time Series Segmentation to Discover Behavior Switching in Complex Physical Systems

    Zheng Han, Lehigh University; Haifeng Chen, NEC Laboratories America; Tan Yan, NEC Laboratories America; Geoff Jiang, NEC Laboratories America

  • Missing Value Estimation for Hierarchical Time Series: A Study of Hierarchical Web Traffic

    Zitao Liu, University of Pittsburgh; Yan Yan, Yahoo! Labs; Milos Hauskrecht, University of Pittsburgh