Zichuan Liu | 刘子川

I am a master student at Nanjing University, supervised by Prof. Chunlin Chen, and I am privileged to work closely with Dr. Tianchun Wang, A/Prof. Dongsheng Luo, and A/Prof. Mengnan Du. I have also spent time at Microsoft Research, advised by Dr. Lei Song and managed by Dr. Jiang Bian. Before that, I was a research intern at DAMO Academy of Alibaba Group, advised by Dr. Qingsong Wen. Previously, I completed my B.S. in Computer Science at Wuhan University of Technology, working with A/Prof. Rui Zhang.

My research interests focus on Reinforcement Learning, eXplainable Artificial Intelligence, and Trustworthy LLMs, especially with applications in multiple sequences, offline models, and dynamic environments, allowing them to explicitly adapt to non-stationary task distributions in real-world.

Note!!! I am actively seeking PhD opportunities beginning in Fall 2025, please contact me if you have a suitable position!

I will pause my research from May to Oct. If have any issues, please email me.

Email  /  CV  /  Google Scholar  /  Github  /  Twitter  /  Linkedin

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Invited Talk

  • "Perturbation-based Techniques for Explaining Sequence Predictions", April, 2024. See This Slide.

  • Research

    Representative papers are highlighted, * denotes contribution equally.

    Protecting Your LLMs with Information Bottleneck
    Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian
    arXiv:2404.13968, 2024   (Under Review)
    project page / paper / code / slides / bibtex

    Our LLM protector provides an effective defence against harmful prompts without losing much of its information!

    Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
    Zhihao Liu, Xianliang Yang, Zichuan Liu, Yifan Xia, Wei Jiang, Yuanyu Zhang, Lijuan Li, Guoliang Fan, Lei Song, Jiang Bian
    arXiv:2405.16854, 2024   (Under Review)
    paper / code / bibtex

    Learning exploration functions to prune multi-agent action spaces by the LLM.

    TimeX++: Learning Time-Series Explanations with Information Bottleneck
    Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo
    International Conference on Machine Learning (ICML), Vienna, Austria, 2024   (CCF-A, CORE A*)
    paper / code / bibtex

    Introduce information theory in explaining time-series.

    Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
    Yifan Xia, Xianliang Yang, Zichuan Liu, Zhihao Liu, Lei Song, Jiang Bian
    International Conference on Machine Learning (ICML), Vienna, Austria, 2024   (Oral Paper, Top 2%, CCF-A, CORE A*)
    paper / code / bibtex

    Doubt about the effectiveness of ML-based heatmap generation in Traveling Salesman Problems.

    Explaining Time Series via Contrastive and Locally Sparse Perturbations
    Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen
    International Conference on Learning Representations (ICLR), Vienna, Austria, 2024   (CORE A*)
    project page / paper / code / slides / bibtex

    A novel perturbation-based method for time series explanation.

    Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
    Linjie Xu, Zichuan Liu, Alexander Dockhorn, Diego Perez-Liebana, Jinyu Wang, Lei Song, Jiang Bian
    IEEE Conference on Games (IEEE CoG), 2024
    paper / code / bibtex

    We look into the training mechanism of MARL and increase replay ratios to enhance sample efficiency.

    RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
    Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Lunting Fan, Lingfei Wu, Qingsong Wen
    Conference on Information and Knowledge Management (CIKM), Boise, USA, 2024   (CCF-B, CORE A*)
    paper / code / bibtex

    We introduce RCAgent, a tool-augmented LLM autonomous agent for cloud root cause analysis.

    NA2Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learing
    Zichuan Liu, Yuanyang Zhu, Chunlin Chen
    International Conference on Machine Learning (ICML), Honolulu, USA, 2023   (CCF-A, CORE A*)
    paper / code / presentation / bibtex

    We present a novel method providing inherent intelligibility of collaboration behavior on multi-agent reinforcement learning.

    Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
    Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, Zhi Wang, Chunlin Chen
    arXiv:2305.07182, 2023   (Under Review)
    paper / code / bibtex

    A transformer framework to significantly improve the performance of multi-agent collaboration.

    MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
    Zichuan Liu, Yuanyang Zhu, Zhi Wang, Yang Gao, Chunlin Chen
    arXiv:2209.07225, 2022   (Under Review)
    paper / code / bibtex

    We propose a novel interpretable architecture that can represent explicit decision processes via the root-to-leaf path of recurrent soft decision trees.

    Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction
    Zichuan Liu, Rui Zhang, Chen Wang, Zhu Xiao, Hongbo Jiang
    IEEE Transactions on Network Science and Engineering (TNSE), 2022   (SCI, JCR-Q1, IF=6.6)
    paper / code / bibtex

    STCL improves traffic prediction by capturing short-term temporal dependencies and accident impact through convolution and sequence learning.

    Multi View Spatial-Temporal Model for Travel Time Estimation
    Zichuan Liu, Zhaoyang Wu, Meng Wang, Rui Zhang
    International Conference on Advances in Geographic Information Systems (SIGSPATIAL), Beijing, China, 2021   (CCF-C, CORE A)
    paper / code / bibtex

    This solution achieved high performance on large-scale taxi trajectory data and won a runner-up award at SIGSPATIAL'2021 GISCUP.

    Experience


    Microsoft Research Asia
    2023.11 - 2024.05
    Research Intern at Machine Learning Group
    Milestones: ICML'24a, ICML'24b, ArXiv'24a, ArXiv'24b, CoG'24
    Research Advisor: Dr. Lei Song and Manager: Dr. Jiang Bian
    Alibaba DAMO Academy & Alibaba Cloud
    2023.06 - 2023.11
    Research Intern
    Milestones: ICLR'24, CIKM'24
    Research Advisor: Dr. Qingsong Wen
    Nanjing University
    2022.09 - Present
    M.S. in Electronic Engineering
    Milestones: ICML'23, ArXiv'22, ArXiv'23
    GPA: 89.38/100, Supervisor: Prof. Chunlin Chen
    Wuhan University of Technology
    2018.09 - 2022.06
    B.S. in Computer Science
    Milestones: IEEE TNSE, SIGSPATIAL'21
    GPA: 91.10/100, Advisor: A/Prof. Rui Zhang

    Service

  • Reviewer, Neural Information Processing Systems (NeurIPS), 2024.
  • Reviewer, IEEE Transactions on Network Science and Engineering (TNSE), 2022.
  • External reviewer, International Conference on Artificial Neural Networks (ICANN), 2022.
  • External reviewer, IEEE Transactions on Computational Social Systems (TCSS), 2022.

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