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Chenjun Xiao
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2020 – today
- 2024
- [c21]Xiaotian Hao, Jianye Hao, Chenjun Xiao, Kai Li, Dong Li, Yan Zheng:
Multiagent Gumbel MuZero: Efficient Planning in Combinatorial Action Spaces. AAAI 2024: 12304-12312 - [c20]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. ICML 2024 - [c19]Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long:
HarmonyDream: Task Harmonization Inside World Models. ICML 2024 - [c18]Yi Ma, Jianye Hao, Hebin Liang, Chenjun Xiao:
Rethinking Decision Transformer via Hierarchical Reinforcement Learning. ICML 2024 - [c17]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. ICML 2024 - [i15]Yangchen Pan, Junfeng Wen, Chenjun Xiao, Philip Torr:
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models. CoRR abs/2404.15518 (2024) - [i14]Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans:
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. CoRR abs/2405.21043 (2024) - [i13]Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai:
Diffusion Spectral Representation for Reinforcement Learning. CoRR abs/2406.16121 (2024) - [i12]Chen-Xiao Gao, Shengjun Fang, Chenjun Xiao, Yang Yu, Zongzhang Zhang:
Hindsight Preference Learning for Offline Preference-based Reinforcement Learning. CoRR abs/2407.04451 (2024) - 2023
- [c16]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. ICLR 2023 - [c15]Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White:
The In-Sample Softmax for Offline Reinforcement Learning. ICLR 2023 - [c14]Hongming Zhang, Chenjun Xiao, Han Wang, Jun Jin, Bo Xu, Martin Müller:
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay. ICLR 2023 - [c13]Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai:
Energy-based Predictive Representations for Partially Observed Reinforcement Learning. UAI 2023: 2477-2487 - [c12]Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran:
Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning. UAI 2023: 2529-2540 - [i11]Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White:
The In-Sample Softmax for Offline Reinforcement Learning. CoRR abs/2302.14372 (2023) - [i10]Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran:
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning. CoRR abs/2303.09032 (2023) - [i9]Xiaohan Hu, Yi Ma, Chenjun Xiao, Yan Zheng, Zhaopeng Meng:
In-Sample Policy Iteration for Offline Reinforcement Learning. CoRR abs/2306.05726 (2023) - [i8]Yi Ma, Chenjun Xiao, Hebin Liang, Jianye Hao:
Rethinking Decision Transformer via Hierarchical Reinforcement Learning. CoRR abs/2311.00267 (2023) - [i7]Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai:
Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. CoRR abs/2311.12244 (2023) - 2022
- [c11]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
The Curse of Passive Data Collection in Batch Reinforcement Learning. AISTATS 2022: 8413-8438 - [c10]Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A. Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans:
Understanding and Leveraging Overparameterization in Recursive Value Estimation. ICLR 2022 - [i6]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. CoRR abs/2212.08765 (2022) - 2021
- [c9]Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. ICML 2021: 11362-11371 - [c8]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. NeurIPS 2021: 19339-19351 - [i5]Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
On the Optimality of Batch Policy Optimization Algorithms. CoRR abs/2104.02293 (2021) - [i4]Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári:
On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. CoRR abs/2106.09973 (2021) - [i3]Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
Understanding the Effect of Stochasticity in Policy Optimization. CoRR abs/2110.15572 (2021) - 2020
- [c7]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. ICML 2020: 6820-6829 - [c6]Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans:
Escaping the Gravitational Pull of Softmax. NeurIPS 2020 - [i2]Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans:
On the Global Convergence Rates of Softmax Policy Gradient Methods. CoRR abs/2005.06392 (2020)
2010 – 2019
- 2019
- [c5]Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller:
On Principled Entropy Exploration in Policy Optimization. IJCAI 2019: 3130-3136 - [c4]Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller:
Maximum Entropy Monte-Carlo Planning. NeurIPS 2019: 9516-9524 - [i1]Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Müller:
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning. CoRR abs/1912.11206 (2019) - 2018
- [c3]Chenjun Xiao, Jincheng Mei, Martin Müller:
Memory-Augmented Monte Carlo Tree Search. AAAI 2018: 1455-1462 - 2017
- [j2]Jiao Wang, Chenjun Xiao, Tan Zhu
, Chu-Hsuan Hsueh, Wen-Jie Tseng, I-Chen Wu
:
Only-One-Victor Pattern Learning in Computer Go. IEEE Trans. Comput. Intell. AI Games 9(1): 88-102 (2017) - 2016
- [c2]Chenjun Xiao, Martin Müller:
Factorization Ranking Model for Move Prediction in the Game of Go. AAAI 2016: 1359-1365 - [c1]Chenjun Xiao, Martin Müller:
Integrating Factorization Ranked Features in MCTS: An Experimental Study. CGW@IJCAI 2016: 34-43 - 2012
- [j1]Jiao Wang, Hongye Li
, Chenjun Xiao, Xin-He Xu:
The 2nd National University Student Computer-Games Tournaments. J. Int. Comput. Games Assoc. 35(3): 182-185 (2012)
Coauthor Index
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last updated on 2024-09-04 00:31 CEST by the dblp team
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