Ph.D. Student, Computer Science and Technology
Tsinghua University
Curriculum Vitae (English) (Chinese)


I'm a third-year Phd 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 served as the editor-in-chief of the CausalAI Community for Beijing Academy of Artificial Intelligence (BAAI). And I'm now the maintainer of the OOD generalization paper list(page).

My research interests include:

  • Out-of-Distribution (OOD) Generalization Problem: algorithms and theoretical Analysis, from both Invariant Learning and Distributionally Robust Optimization
  • Data Heterogeneity Measure
  • Robust Learning in Single-Cell RNA Sequence Data Analysos

And I am willing to collaborate with people from different fields to promote the application of OOD generalization problems in other fields. Feel free to contact me!

Email: liujiashuo77@gmail.com


Publications

2022

  1. [13]
    Jiashuo Liu, Jiayun Wu, Jie Peng, Zheyan Shen, Bo Li and Peng Cui. Distributionally Invariant Learning: Rationalization and Practical Algorithms. Under Review, [paper]
  2. [12]
    Zheyan Shen*, Jiashuo Liu*, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui. Towards Out-of-Distribution Generalization: A Survey Under Review at IEEE TPAMI, [paper] [website]
  3. [11]
    Xingxuan Zhang, Zekai Xu, Renzhe Xu, Jiashuo Liu, Peng Cui, Weitao Wan, Chong Sun, Chen Li. Towards Domain Generalization in Object Detection Under Review,
  4. [10]
    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.
  5. [9]
    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]
  6. [8]
    Dacheng Xu, Benda Xu, Erjin Bao, Yiyang Wu, Aiqiang Zhang, Yuyi Wang, Geliang Zhang, Yu Xu, Ziyi Guo, Jihui Pei, Hanyang Mao, Jiashuo Liu, Zhe Wang, Shaomin Chen. Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments JINST 2022, In Journal of Instrumentation. [paper]
  7. [7]
    Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip Yu, Peng Cui Invariant Preference Learning for General Debiasing in Recommendation KDD 2022, In SIGKDD Conference on Knowledge Discovery and Data Mining.

2021

  1. [6]
    Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang Triple Generative Adversarial Networks. TPAMI 2021, In Transactions on Pattern Analysis and Machine Intelligence. [paper]
  2. [5]
    Jiashuo Liu*, Zheyuan Hu*, Peng Cui, Bo Li, Zheyan Shen Kernelized Heterogeneous Risk Minimization NeurIPS 2021, In Neural Information Processing Systems. [paper] [code]
  3. [4]
    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]
  4. [3]
    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]
  5. [2]
    Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu Signed Graph Neural Network with Latent Groups KDD 2021, In SIGKDD Conference on Knowledge Discovery and Data Mining. [paper]

2020

  1. [1]
    Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li, Zhitang Chen Stable Learning via Differentiated Variable Decorrelation KDD 2020, In SIGKDD Conference on Knowledge Discovery and Data Mining. [paper]

Talks and Presentations

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]

From Latent Heterogeneity to Out-of-Distribution Generalization. [ppt]

  • Invited by Badong Chen's group, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Nov. 26, 2021 (Online)
  • Invited by the Beijing Academy of Artificial Intelligence (BAAI), Nov. 5, 2021 (Online)

Heterogeneous Risk Minimization. [ppt]

  • Invited by Kun Kuang's group, Lab of Artificial Intelligence, Zhejiang University, Oct. 18, 2021 (Online)


Professional Services

Journal reviewer: TMM

Conference reviewer / Program committee: ICML (2022), UAI (2022), AAAI (2022), IJCAI (2022), CVPR (2022), CoLLAs (2022), AISTATS (2023), CVPR (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, TA)
  • Object-oriented Programming (Summar 2022, TA)

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