Ph.D. Candidate, Computer Science and Technology
Tsinghua University

Curriculum Vitae (English/ Chinese)

Email: liujiashuo77@gmail.com

I am a fourth-year Ph.D. student in the Department of Computer Science and Technology at Tsinghua University, advised by Prof. Peng Cui and Prof. Bo Li. I got my Bachelor degree in the Department of Computer Science and Technology at Tsinghua University in 2020. I work closely with Prof. Hongseok Namkoong in the Decision, Risk, and Operations division at Columbia Business School (remote). I also collaborate with Prof. Kun Kuang at Zhejiang University.

My research interests include:

  • Evaluation under Distribution Shifts.
  • Measure, Explore, and Exploit Data Heterogeneity.
  • Distributionally Robust Optimization.
  • Applications of OOD Generalization & Heterogeneity.

I am looking for undergraduates to collaborate with. If you are interested in performance evaluation, robust learning, out-of-distribution generalization, etc. Please fill in this form.

Selected Tutorials

Selected Papers
The full list of publications can be found in the publications page.

* indicates equal contributions.

Evaluation under Distribution Shifts

  1. [2]
    Jiashuo Liu*, Tianyu Wang*, Peng Cui, Hongseok Namkoong On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets NeurIPS 2023, Datasets and Benchmarks Track
    The 37th Conference on Neural Information Processing Systems. [paper][code] [python package] [downloads>2.3k]
  2. [1]
    Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui. Rethinking the Evaluation Protocol of Domain Generalization Under Review, [paper]

Explore and Exploit Data Heterogeneity

  1. [5]
    Jiashuo Liu*, Zheyan Shen*, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui. Towards Out-of-Distribution Generalization: A Survey Under Review at IEEE TPAMI, [paper] [website] [pageviews>19k]
  2. [4]
    Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui. Predictive Heterogeneity: Measures and Applications Revise & Resubmit at JMLR, [paper]
  3. [3]
    Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li and Peng Cui Measure the Predictive Heterogeneity ICLR 2023, The 11th International Conference on Learning Representations. [paper]
  4. [2]
    Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen Kernelized Heterogeneous Risk Minimization NeurIPS 2021, In Neural Information Processing Systems. [paper] [code]
  5. [1]
    Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen Heterogeneous Risk Minimization ICML 2021, In International Conference on Machine Learning. Short talk (Top 21.5%). [paper] [code]

Distributionally Robust Optimization

  1. [3]
    Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang and Bo Li Distributionally Robust Learning with Stable Adversarial Training IEEE TKDE 2022, In IEEE Transactions on Knowledge and Data Engineering.
  2. [2]
    Jiashuo Liu*, Jiayun Wu*, Bo Li and Peng Cui. Distributionally Robust Optimization with Data Geometry NeurIPS 2022, In Neural Information Processing Systems. Spotlight presentation (Top 5.2%). [paper]
  3. [1]
    Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li and Yishi Lin Stable Adversarial Learning under Distributional Shifts AAAI 2021, In AAAI Conference on Artificial Intelligence. [paper] [code]

Patents
  1. ●
    Peng Cui, Jiashuo Liu. Distribution robustness adversarial learning method. Issued 2022. CN Patent: CN 112085194 B, filed August 30, 2020, and issued December 13, 2022. [certificate]
  2. ●
    Peng Cui, Jiashuo Liu. Invariant learning method and device based on heterogeneity hybrid data. Issued 2023. CN Patent: CN 113205184 B, filed April 28, 2021, and issued January 31, 2023. [certificate]

Selected Talks

Predictive Heterogeneity: Measures and Applications

  • Invited by Prof. Kun Kuang's group, Lab of Artificial Intelligence, Zhejiang University, Mar. 15, 2023 [ppt]

Measure the Predictive Heterogeneity

Distributionally Robust Optimization with Data Geometry

Data Heterogeneity and Invariance in Out-of-Distribution Generalization

Mining the Data Heterogeneity for Out-of-Distribution Generalization

  • Invited by Dr Tat-Seng Chua's group, NUS-Tsinghua Extreme Search Center (NExT), June. 08, 2022 (Online) [ppt]
  • Invited by Dr Kun Kuang's group, Lab of Artificial Intelligence, Zhejiang University, Sep. 1, 2022 [ppt]

Professional Services

Journal reviewer: Operations Research, IEEE Transactions on Multimedia

Conference reviewer / Program committee: ICLR (2024), NeurIPS (2023), ICML (2022,2023), UAI (2022,2023), AAAI (2022), IJCAI (2022,2023), CVPR (2022,2023), ICCV(2023), CoLLAs (2022,2023), AISTATS (2021,2023,2024)

Workshop reviewer / Program committee: NeurIPS DistShift (2023)

Awards and Fellowships
  • Excellent Comprehensive Scholarship of Tsinghua University (for PhD. Student, 2022)
  • National Scholarship for Graduate Student (2021)
  • Apple Scholars in AI/ML Nomination (2021)
  • Excellent Undergraduate, Tsinghua University (2020, 10%)
  • TP-Link Scholarship (2019)
  • Toyota Scholarship (2018)
  • Excellent Academic Scholarship of Tsinghua University (2018, 2019)
  • Excellent Comprehensive Scholarship of Tsinghua University (2017, 5%)
  • Second-Class Freshmen Scholarship of Tsinghua University (2016~2019)
Teaching
  • Software Engineering (Fall 2019, 2020, 2021, 2022, Spring 2022, 2023 TA)
  • Object-oriented Programming (Summer 2022, TA)
Interesting Projects
  • Remove the watermark of a PDF file. [GitHub Code]
  • An AI player for the Connect Four Game (ranked 100/715 in the leaderboard, Top 14%). [Leaderboard] [Human vs AI]: find 2022AI_2020310840 and press the right button

Acknowledgements: based on the al-folio template by Maruan Al-Shedivat and Jiaming Song

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