Shenzhi Wang

Shenzhi Wang

PhD Candidate of Artificial Intelligence

Tsinghua University

Biography

I’m Shenzhi Wang (王慎执 in Chinese), a third-year Ph.D. student at LEAP Lab in the Department of Automation at Tsinghua University, advised by Prof. Shiji Song and Prof. Gao Huang. Before that, I received my B.E. degree (with honors) at SHENYUAN Honors College of Beihang University, majoring in Computer Science and Technology.

In my spare time, I write some Chinese tech blogs on Zhihu (知乎) and have 5k+ followers. Welcome to follow my Zhihu account!

Important: I am actively looking for a visiting student position for the Fall 2024 semester. If you have any available positions, I would be very interested. Please feel free to contact me via wangshenzhi99@gmail.com!

Interests
  • Reinforcement Learning
  • LLM-as-Agent
  • Computer Vision
Education
  • PhD Student in Artificial Intelligence, 2021 - Present

    Department of Automation, Tsinghua University

  • B.Eng. in Computer Science and Technology, 2017 - 2021

    SHENYUAN Honors College, Beihang University

News

  • Oct 02, 2023 We have released a preprint paper about LLMs dealing with deceptions in the Avalon game; see this Arxiv link.
  • Sep 22, 2023 One paper is accepted by NeurIPS 2023 as Spotlight; see this Arxiv link.
  • Jul 03, 2023 One paper is accepted by IEEE TNNLS!
  • Apr 23, 2023 One paper is accepted by ICML 2023!
  • Mar 01, 2021 One paper is accepted to CVPR 2021!
  • Dec 09, 2020 Honored to be one of 21 undergraduates awarded SenseTime Scholarship, 2020.

    Publications

    Quickly discover relevant content by filtering publications.
    (2023). Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation. Arxiv preprint.

    Arxiv Website 机器之心报道 新智元报道 量子位报道

    (2023). Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning. In NeurIPS (Spotlight).

    Cite Arxiv Website Code

    (2023). Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance. IEEE TNNLS.

    Cite Arxiv

    (2023). Boosting Offline Reinforcement Learning with Action Preference Query. In ICML.

    Cite Arxiv

    (2021). Glancing at the Patch: Anomaly Localization with Global and Local Feature Comparison. In CVPR.

    Cite Paper

    Academic Services

    Reviewers for NeurIPS 2023, ICLR 2024, and ICRA 2024.

      Contact

      For more information about my work or to learn more about me, please don’t hesitate to reach out via email or in person.

      • wangshenzhi99@gmail.com (preferred), wsz21@mails.tsinghua.edu.cn
      • 616 Center Main Building, Tsinghua University, Beijing 100084, China.