Xiaorui Liu

Email: xiaorui@msu.edu

Office: 3308 Engineering Building

Link:            

Lab: Data Science & Engineering Lab      

About Me

I'm a fifth-year Ph.D. student in the Department of Computer Science and Engineering at Michigan State University. I am fortunately advised by Prof. Jiliang Tang in the Data Science and Engineering Lab, and I also work closely with Prof. Ming Yan from Department of Computational Mathematics, Science and Engineering (CMSE). Before that, I received both of my bachelor and master degree from South China University of Technology.

My research interests are machine learning and optimization, especially large-scale distributed optimization, robust and secure machine leaning, and machine learning on graphs.

I will be a tenure-track Assistant Professor of Computer Science at North Carolina State University (NCSU) starting from Fall 2022. If you are interested in working with me, please feel free to contact me.

News

• 05/2022 Our paper Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective is acceptd in KDD 2022.
• 05/2022 Our paper Trustworthy AI: A Computational Perspective is acceptd in ACM Transactions on Intelligent Systems and Technology (TIST).
• 05/2022 I am invited to give a talk in the ML Seminar at Vanderbilt University on May 16.
• 04/2022 I am invited by TechBeat to give an online talk about Communication-Efficient Distributed Machine Learning on April 28th [Video Link].
• 04/2022 I am invited to serve as a Session Chair for SDM 2022.
• 04/2022 Our paper Graph Trend Filtering Networks for Recommendations is accepted in SIGIR 2022.
• 03/2022 I am invited to serve as the reviewer for NeurIPS 2022.
• 02/2022 I am invited to serve as the PC member for KDD 2022 and CIKM 2022.
• 01/2022 Two papers are accepted in ICLR 2022.
  Is Homophily a Necessity for Graph Neural Networks?
  Automated Self-Supervised Learning for Graphs
• 12/2021 Our tutorial on "Trustworthy AI: A Computational Perspective" is accepted in the Web Conference (WWW 2022).
• 12/2021 One paper "Learning from Imbalanced Crowdsourced Labeled Data" is accepted in SDM 2022.
• 12/2021 I am invited to serve as the reviewer for ICML 2022.
• 10/2021 I have been selected to be the volunteer for NeurIPS 2021.
• 09/2021 Our paper Graph Neural Networks with Adaptive Residual is accepted in NeurIPS 2021.
• 08/2021 Received the MSU Cloud Computing Fellowship.
• 08/2021 Present a tutorial about Communication Efficient Distributed Learning at IJCAI 2021. Please check the website and slide for details.
• 08/2021 Our new preprint Decentralized Composite Optimization with Compression is online.
• 08/2021 Three papers are accepted in CIKM 2021!
• 08/2021 I am invited to serve as the PC member for AAAI 2022.
• 08/2021 Present two tutorials about Graph Representation Learning and Adversarial Robustness at KDD 2021.
• 08/2021 Present one tutorial about Trustworthy AI at ICAPS 2021.
• 07/2021 Our new preprint Trustworthy AI: A Computational Perspective is online.
• 07/2021 Our tutorial on Trustworthy AI: A Computational Perspective is accepted to be held in ICAPS 2021.
• 07/2021 Give a long presentation about Elastic GNN at ICML 2021. Welcome to check the paper, slide, poster, and code for details.
• 07/2021 I am invited to serve as a member of the novel Program Committee Board (PCB) of IJCAI.

[More News]

• 06/2021 Our new preprint Is Homophily a Necessity for Graph Neural Networks? is online.
• 06/2021 Our new preprint Automated Self-Supervised Learning for Graphs is online.
• 06/2021 Our new preprint Towards the Memorization Effect of Neural Networks in Adversarial Training is online.
• 06/2021 I am invited to serve as the Volunteer for ICML 2021.
• 06/2021 I am invited to server as PC members for ICLR 2022 and WSDM 2022.
• 05/2021 Our new preprint Graph Feature Gating Networks is online.
• 05/2021 Two papers are accepted in ICML 2021!
  Elastic Graph Neural Networks is accepted for oral (long) presentation (3% ≈ 166/5513).
  To be Robust or to be Fair: Towards Fairness in Adversarial Training is accepted for spotlight presentation (21% ≈ 1184/5513).
• 05/2021 Two tutorials are accepted to be held in KDD 2021.
  Graph Representation Learning: Foundations, Methods, Applications and Systems
  Adversarial Robustness in Deep Learning: From Practices to Theories
• 05/2021 Present our work on distributed machine learning in 2021 MSU Engineering Graduate Research Symposium.
• 05/2021 Present our work Linear Convergent Decentralized Optimization with Compression in ICLR virtual conference. We show a decentralized optimization algorithm that works perfectly with communication compression. Check the paper, slide, and poster for details.
• 04/2021 Our research on large-scale machine learning is covered by The Institute for Cyber-Enabled Research (ICER at MSU), which provides a solid infrastructure with advanced computational systems such as high performance computing platforms (HPCC). Refer to the newsletter Faster Distributed Machine Learning for Free for more details.
• 04/2021 Our tutorial on Communication Efficient Distributed Learning is accepted to be held in IJCAI 2021. The tutorial website is under construction.
• 04/2021 I am invited to serve as PC members for NeurIPS 2021 and CIKM 2021.
• 04/2021 I am invited to serve as the Research Track session chair (on graph algorithms) for The Web Conference 2021.
• 03/2021 I am invited to serve as the Volunteer for ICLR 2021.
• 03/2021 I am honored to receive the Student Scholarship Award from The Web Conference 2021.
• 01/2021 Our paper Linear Convergent Decentralized Optimization with Compression is accepted by ICLR 2021.
• 01/2021 Our paper Yet Meta Learning Can Adapt Fast, it Can Also Break Easily is accepted by SDM 2021.
• 12/2020 I was invited to serve as a reviewer for ICML 2021.
• 12/2020 I was invited to serve as a senior PC member for IJCAI 2021.
• 10/2020 Our new work A Unified View on Graph Neural Networks as Graph Signal Denoising is online.
• 10/2020 Our new work To be Robust or to be Fair: Towards Fairness in Adversarial Training is online.
• 08/2020 I was invited to serve as PC members for WWW 2021, AAAI 2021.
• 07/2020 Our paper Linear Convergent Decentralized Optimization with Compression is online. This is the first algorithm achieving linear convergence with communication compression in decentralized optimization. Welcome to check it!
• 06/2020 I was invited as a PC member for CIKM 2020.
• 06/2020 I start my research internship at Kwai AI Lab working with Dr. Xiangru Lian and Dr. Ji Liu.
• 05/2020 Our paper Graph Structure Learning for Robust Graph Neural Networks is accepted by KDD 2020.
• 04/2020 I was invited as PC members for NeurIPS 2020 and ICONIP 2020.
• 01/2020 Our paper A Double Residual Compression Algorithm for Efficient Distributed Learning is accepted by AISTATS 2020.
• Our paper Deep Adversarial Canonical Correlation Analysis is accepted by SDM 2020.
• Our paper A Double Residual Compression Algorithm for Efficient Distributed Learning for highly efficient distributed optimization is online.
• Our paper Epidemic Graph Convolutional Network for efficient training of GCN using epidemic modeling is accepted by WSDM 2020!
• Our paper Deep Adversarial Network Alignment for network alignment via deep adversarial generative model is online.
• Our paper Weight Loss Prediction in Social-Temporal Context received ICHI 2019 Best Paper Honorable Mention Award!
• Our paper A Survey on Dialogue Systems: Recent Advances and New Frontiers is accepted by ACM SIGKDD Explorations Newsletter!

Experience

• Research Intern: Kwai AI Lab, May-Aug 2020
  Mentored by Dr. Ji Liu and Dr. Xiangru Lian
  Communication Efficient Distributed Optimization
• Research Intern: Data Science Lab @ JD.com, May-Aug 2018
  Mentored by Dr. Dawei Yin and Dr. Weixue Lu
  Primal-Dual Analyses of Distributed Optimization Algorithms via Dynamic System and Robust Control Theory
• Research Intern: Data Science Lab @ JD.com, Jul-Sep 2017
  Mentored by Dr. Hongshen Chen and Dr. Ziheng Jiang
  Product Words Recommendation in Large-scale E-commerce Platform
• Research Assistant: Deep Learning & Vision Computing Lab @ SCUT, Sep 2014-Jun 2017
  Mentored by Prof. Lianwen Jin and Prof. Xin Zhang
  Visual Object Recognition and Detection with Deep Learning

Tutorial

• Communication Efficient Distributed Learning
  Xiaorui Liu, Yao Li, Ming Yan, Jiliang Tang.
  International Joint Conference on Artificial Intelligence (IJCAI 2021)
  [ website ] [slide]
• Adversarial Robustness in Deep Learning: From Practices to Theories
  Han Xu, Yaxin Li, Xiaorui Liu, Wentao Wang, Jiliang Tang
  International Conference on Knowledge Discovery & Data Mining (KDD 2021)
  [website] [slide]
• Graph Representation Learning: Foundations, Methods, Applications and Systems
  Wei Jin, Yao Ma, Yiqi Wang, Xiaorui Liu, Jiliang Tang
  International Conference on Knowledge Discovery & Data Mining (KDD 2021)
  [website] [slide]
• Trustworthy AI: A Computational Perspective
  Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Jiliang Tang.
  International Conference on Automated Planning and Scheduling (ICAPS 2021)
  [ website ] [ slide ]

Publication

       [* indicates equal contribution]    My Google scholar page

       Preprints and Submissions

• Decentralized Composite Optimization with Compression
  Xiaorui Liu*, Yao Li*, Jiliang Tang, Ming Yan, Kun Yuan.
  arXiv 2108.04448
  
• Towards the Memorization Effect of Neural Networks in Adversarial Training
  Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang
  arXiv 2106.04794
  
• Imbalanced Adversarial Training with Reweighting
  Wentao Wang, Han Xu, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, Jiliang Tang.
  arXiv 2107.13639
  
• Jointly Attacking Graph Neural Network and its Explanations
  Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, Jianping Wang, Charu Aggarwal.
  arXiv 2108.03388
  

       Conference and Journal Publications

• Trustworthy AI: A Computational Perspective
  Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang.
  ACM Transactions on Intelligent Systems and Technology (TIST 2022)
  
• Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
  Wei Jin*, Xiaorui Liu*,Yao Ma, Charu Aggarwal, Jiliang Tang.
  ACM International Conference on Knowledge Discovery & Data Mining (KDD 2022)
  
• Graph Trend Filtering Networks for Recommendations
  Xiaorui Liu*, Wenqi Fan*, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li.
  ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022)
  
• Automated Self-Supervised Learning for Graphs
  Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang.
  International Conference on Learning Representations (ICLR 2022)
  [ paper ]
• Is Homophily a Necessity for Graph Neural Networks?
  Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang.
  International Conference on Learning Representations (ICLR 2022)
  [ paper ]
• Learning from Imbalanced Crowdsourced Labeled Data
  Wentao Wang, Joseph Thekinen, Xiaorui Liu, Zitao Liu, Jiliang Tang.
  SIAM International Conference on Data Mining (SDM 2022).
• Graph Neural Networks with Adaptive Residual
  Xiaorui Liu, Jiayuan Jin, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang.
  Conference on Neural Information Processing Systems (NeurIPS 2021)
  [ paper ] [ appendix ] [ slide ] [ poster ] [ code ] [ bibtex ]
• Graph Feature Gating Network
  Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang.
  The Conference on Information and Knowledge Management (CIKM 2021)
  [ paper ] [ bibtex ]
• A Unified View on Graph Neural Networks as Graph Signal Denoising
  Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah.
  The Conference on Information and Knowledge Management (CIKM 2021)
  [ paper ] [ bibtex ]
• Deep Adversarial Network Alignment
  Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, Jiliang Tang.
  The Conference on Information and Knowledge Management (CIKM 2021)
  [ paper ] [ bibtex ]
• Elastic Graph Neural Networks
  Xiaorui Liu*, Wei Jin*, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang.
  International Conference on Machine Learning (ICML 2021)
  Oral (3% ≈ 166/5513)
  [ paper ] [ video ] [ slide ] [ poster ] [ code ] [ bibtex ]
• To be Robust or to be Fair: Towards Fairness in Adversarial Training
  Xiaorui Liu*, Han Xu*, Yaxin Li, Anil Jain, Jiliang Tang.
  International Conference on Machine Learning (ICML 2021)
  Spotlight (21% ≈ 1184/5513)
  [ paper ] [ video ] [ slide ] [ bibtex ]
• Linear Convergent Decentralized Optimization with Compression
  Xiaorui Liu, Yao Li, Rongrong Wang, Jiliang Tang, Ming Yan.
  International Conference on Learning Representations (ICLR 2021)
  Score ranking (3% ≈ 77/2976)
  [ paper ] [ slide ] [ poster ] [ video ] [ bibtex ]
• Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
  Han Xu, Yaxin Li, Xiaorui Liu, Hui Liu, Jiliang Tang.
  SIAM International Conferenceon Data Mining (SDM 2021)
  [ paper ] [ bibtex ]
• A Double Residual Compression Algorithm for Efficient Distributed Learning
  Xiaorui Liu, Yao Li, Jiliang Tang and Ming Yan.
  International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
  [ abs ] [ paper ] [ supplementary ] [ slide ] [ video ] [ bibtex ]
• Graph Structure Learning for Robust Graph Neural Networks
  Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang.
  ACM International Conference on Knowledge Discovery & Data Mining (KDD 2020)
  [ paper ] [ code ] [ slide ] [ bibtex ]
• Deep Adversarial Canonical Correlation Analysis
  Wenqi Fan, Yao Ma, Han Xu, Xiaorui Liu, Jianping Wang, Qing Li, and Jiliang Tang.
  SIAM International Conferenceon Data Mining (SDM 2020)
  [ paper ] [ bibtex ]
• Epidemic Graph Convolutional Network
  Tyler Derr, Yao Ma, Wenqi Fan, Xiaorui Liu, Charu Aggarwal and Jiliang Tang.
  ACM International Conference on Web Search and Data Mining (WSDM 2020)
  [ paper ] [ code ] [ bibtex ]
• Weight Loss Prediction in Social-Temporal Context
  Zhiwei Wang, Xiaorui Liu, Jiliang Tang and Dawei Yin.
  IEEE International Conference on Healthcare Informatics (ICHI 2019)
  [ paper ] [ bibtex ]
  Best Paper Honorable Mention Award
  
• A Survey on Dialogue Systems: Recent Advances and New Frontiers
  Hongshen Chen, Xiaorui Liu, Dawei Yin and Jiliang Tang.
  ACM SIGKDD Explorations Newsletter (SIGKDD Explorations 2017).
  [ paper ] [ bibtex ]
• Fingertip in the Eye: An Attention-Based Method for Real-Time Hand Tracking and Fingertip Detection in Egocentric Videos
  Xiaorui Liu, Yichao Huang, Xin Zhang and Lianwen Jin.
  Chinese Conference on Pattern Recognition (CCPR 2016).
• A Pointing Gesture based Egocentric Interaction System: Dataset, Approach and Application
  Yichao Huang, Xiaorui Liu, Xin Zhang and Lianwen Jin.
  CVPR Workshop 2016. Check out our demo for Real-time Air Writing System!
• Deepfinger: A Cascade Convolutional Neural Network Approach to Finger Key Point Detection in Egocentric Vision with Mobile Camera
  Yichao Huang, Xiaorui Liu, Lianwen Jin and Xin Zhang.
  IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015).

Teaching

• Teaching Assistant for CMSE 890 (Graduate Level) Optimization, 2019 Fall
• Invited Lecturer for CSE 881 (Graduate Level) Data Mining, 2018 Fall
• Teaching Assistant for CSE 260 (Undergraduate Level) Discrete Structures, 2019 Spring
• Teaching Assistant for CSE 260 (Undergraduate Level) Discrete Structures, 2018 Fall

Award

• Free Registration on Virtual Conferences for ICML'21, ICLR'21, WWW'21, KDD'21, IJCAI'21, ICAPS'21, AISTATS'20
• MSU Cloud Computing Fellowship, 2021
• MSU COGS Conference Award, 2021
• The Web Conference Student Scholarship Award, 2021
• Best Paper Honorable Mention Award, ICHI'19
• MSU Graduate School Travel Fellowship, 2019
• MSU Graduate School Travel Fellowship, 2018
• Student Travel Award for WSDM'18, 2018
• MSU Engineering Distinguished Fellowship, 2017

• First Prize Scholarship, SCUT, 2016
• First Place in Hand Detection Challenge in CVPR VIVA, 2016
• The First Prize in Digital Image Processing Contest, Guangdong, China, 2015
• “Goodix” Industry Scholarship, 2014
• “Endress+Hauser” Industry Scholarship, 2013
• “Hongsheng Huang” Science and Technology Innovation Scholarship, 2013
• National Endeavor Scholarship (2 times) 2012 and 2013

Service

• Senior Program Committee Members: IJCAI'21
• Program Committee Members: ICLR'22, AAAI'22, WSDM'22, NeurIPS'21, ICML'21, CIKM'21, WWW'21, AAAI'21, CIKM'20, NeurIPS'20, ICONIP'20
• External Reviewers: KDD'21, ICLR'20, KDD'20, ICML'20, NeurIPS'19, ASONAM'19, CIKM'19, ICWSM'19, IJCAI'19, SIGIR'19, ASONAM'18, CIKM'18, KDD'18, RecSys'18, AAAI'18, WWW'18, SIGIR'18, WSDM'18
• Conference Volunteers: WSDM'18, ICML'20, ICLR'21, ICML'21
• Session Chair: WWW'21
• University Service: Committee Member on MSU Engineering College Advisory Council, 2021-2022