Zichuan Liu

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Citations	48	48
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i10-index	1	1

2022202320245 11 32

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Lei SongMicrosoft Research AsiaVerified email at microsoft.com
Zefan WangTsinghua UniversityVerified email at mails.tsinghua.edu.cn
Yuanyang ZhuNanjing UniversityVerified email at smail.nju.edu.cn
Tianchun WangThe Pennsylvania State UniversityVerified email at psu.edu
Hongbo JiangHunan UniversityVerified email at hnu.edu.cn
Qingsong Wen (文青松)Head of AI Research & Chief Scientist @ Squirrel AIVerified email at gatech.edu
Jiang BianMicrosoft ResearchVerified email at microsoft.com
Chunlin ChenNanjing UniversityVerified email at nju.edu.cn
Dongsheng LuoAssistant Professor, Florida International UniversityVerified email at fiu.edu
Linjie XuQueen Mary University of LondonVerified email at qmul.ac.uk
Aoxiao ZhongSchool of Engineering and Applied Sciences (SEAS) at Harvard UniversityVerified email at g.harvard.edu
Yang GaoNanjing University, ChinaVerified email at nju.edu.cn
Wei ChengSenior Research Staff Member, NEC Laboratories America (Ph.D, UNC-Chapel Hill)Verified email at nec-labs.com

Zichuan Liu

Nanjing University

Verified email at smail.nju.edu.cn - Homepage

Explainable AI Trustworthy LLM Reinforcement Learning


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Spatial-temporal conv-sequence learning with accident encoding for traffic flow prediction Z Liu, R Zhang, C Wang, Z Xiao, H Jiang IEEE Transactions on Network Science and Engineering 9 (3), 1765-1775, 2022	20	2022
Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models Z Wang, Z Liu, Y Zhang, A Zhong, L Fan, L Wu, Q Wen arXiv preprint arXiv:2310.16340, 2023	5	2023
N Q: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning Z Liu, Y Zhu, C Chen ICML 2023, 2023	5	2023
Multi-view spatial-temporal model for travel time estimation Z Liu, Z Wu, M Wang, R Zhang SIGSPATIAL 2021, 2021	5	2021
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees Z Liu, Y Zhu, Z Wang, Y Gao, C Chen arXiv preprint arXiv:2209.07225, 2022	4	2022
Protecting your llms with information bottleneck Z Liu, Z Wang, L Xu, J Wang, L Song, T Wang, C Chen, W Cheng, J Bian arXiv preprint arXiv:2404.13968, 2024	3	2024
Explaining Time Series via Contrastive and Locally Sparse Perturbations Z Liu, Y Zhang, T Wang, Z Wang, D Luo, M Du, M Wu, Y Wang, C Chen, ... ICLR 2024, 2024	3	2024
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems Y Xia, X Yang, Z Liu, Z Liu, L Song, J Bian ICML 2024 Oral, 2024	1	2024
TimeX++: Learning Time-Series Explanations with Information Bottleneck Z Liu, T Wang, J Shi, X Zheng, Z Chen, L Song, W Dong, J Obeysekera, ... ICML 2024, 2024	1	2024
Boosting value decomposition via unit-wise attentive state representation for cooperative multi-agent reinforcement learning Q Zhao, Y Zhu, Z Liu, Z Wang, C Chen arXiv preprint arXiv:2305.07182, 2023	1	2023
Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning Z Liu, X Yang, Z Liu, Y Xia, W Jiang, Y Zhang, L Li, G Fan, L Song, B Jiang arXiv preprint arXiv:2405.16854, 2024		2024
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning L Xu, Z Liu, A Dockhorn, D Perez-Liebana, J Wang, L Song, J Bian IEEE CoG 2024, 2024		2024

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