The 2nd National University Student Computer-Games Tournaments. - Jiao Wang; Hongye Li; Chenjun Xiao; Xin-He Xu (J. Int. Comput. Games Assoc., 2012)

Factorization Ranking Model for Move Prediction in the Game of Go. - Chenjun Xiao; Martin Müller 003 (AAAI, 2016)

Integrating Factorization Ranked Features in MCTS: An Experimental Study. - Chenjun Xiao; Martin Müller 003 (CGW@IJCAI, 2016)

Only-One-Victor Pattern Learning in Computer Go. - Jiao Wang 005; Chenjun Xiao; Tan Zhu; Chu-Hsuan Hsueh; Wen-Jie Tseng 001; I-Chen Wu (IEEE Trans. Comput. Intell. AI Games, 2017)

Memory-Augmented Monte Carlo Tree Search. - Chenjun Xiao; Jincheng Mei; Martin Müller 003 (AAAI, 2018)

On Principled Entropy Exploration in Policy Optimization. - Jincheng Mei; Chenjun Xiao; Ruitong Huang; Dale Schuurmans; Martin Müller 003 (IJCAI, 2019)

Maximum Entropy Monte-Carlo Planning. - Chenjun Xiao; Ruitong Huang; Jincheng Mei; Dale Schuurmans; Martin Müller 003 (NeurIPS, 2019)

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning. - Chenjun Xiao; Yifan Wu; Chen Ma; Dale Schuurmans; Martin Müller 003 (CoRR, 2019)

On the Global Convergence Rates of Softmax Policy Gradient Methods. - Jincheng Mei; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (ICML, 2020)

Escaping the Gravitational Pull of Softmax. - Jincheng Mei; Chenjun Xiao; Bo Dai 001; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2020)

On the Optimality of Batch Policy Optimization Algorithms. - Chenjun Xiao; Yifan Wu; Jincheng Mei; Bo Dai 001; Tor Lattimore; Lihong Li 001; Csaba Szepesvári; Dale Schuurmans (ICML, 2021)

Understanding the Effect of Stochasticity in Policy Optimization. - Jincheng Mei; Bo Dai 001; Chenjun Xiao; Csaba Szepesvári; Dale Schuurmans (NeurIPS, 2021)

On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. - Chenjun Xiao; Ilbin Lee; Bo Dai 001; Dale Schuurmans; Csaba Szepesvári (CoRR, 2021)

The Curse of Passive Data Collection in Batch Reinforcement Learning. - Chenjun Xiao; Ilbin Lee; Bo Dai 001; Dale Schuurmans; Csaba Szepesvári (AISTATS, 2022)

Understanding and Leveraging Overparameterization in Recursive Value Estimation. - Chenjun Xiao; Bo Dai 001; Jincheng Mei; Oscar A. Ramirez; Ramki Gummadi; Chris Harris 001; Dale Schuurmans (ICLR, 2022)

Latent Variable Representation for Reinforcement Learning. - Tongzheng Ren; Chenjun Xiao; Tianjun Zhang; Na Li 002; Zhaoran Wang 001; Sujay Sanghavi; Dale Schuurmans; Bo Dai 001 (ICLR, 2023)

The In-Sample Softmax for Offline Reinforcement Learning. - Chenjun Xiao; Han Wang; Yangchen Pan; Adam White 001; Martha White (ICLR, 2023)

Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay. - Hongming Zhang 003; Chenjun Xiao; Han Wang; Jun Jin 001; Bo Xu 002; Martin Müller 003 (ICLR, 2023)

Energy-based Predictive Representations for Partially Observed Reinforcement Learning. - Tianjun Zhang; Tongzheng Ren; Chenjun Xiao; Wenli Xiao; Joseph E. Gonzalez; Dale Schuurmans; Bo Dai 001 (UAI, 2023)

Conditionally optimistic exploration for cooperative deep multi-agent reinforcement learning. - Xutong Zhao; Yangchen Pan; Chenjun Xiao; Sarath Chandar; Janarthanan Rajendran (UAI, 2023)

In-Sample Policy Iteration for Offline Reinforcement Learning. - Xiaohan Hu; Yi Ma 005; Chenjun Xiao; Yan Zheng 002; Zhaopeng Meng (CoRR, 2023)

Multiagent Gumbel MuZero: Efficient Planning in Combinatorial Action Spaces. - Xiaotian Hao; Jianye Hao; Chenjun Xiao; Kai Li 022; Dong Li 016; Yan Zheng 002 (AAAI, 2024)

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation. - Fengdi Che; Chenjun Xiao; Jincheng Mei; Bo Dai 001; Ramki Gummadi; Oscar A. Ramirez; Christopher K. Harris; A. Rupam Mahmood; Dale Schuurmans (ICML, 2024)

HarmonyDream: Task Harmonization Inside World Models. - Haoyu Ma; Jialong Wu 001; Ningya Feng; Chenjun Xiao; Dong Li 016; Jianye Hao; Jianmin Wang 001; Mingsheng Long (ICML, 2024)

Rethinking Decision Transformer via Hierarchical Reinforcement Learning. - Yi Ma 005; Jianye Hao; Hebin Liang; Chenjun Xiao (ICML, 2024)

Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning. - Hongming Zhang 012; Tongzheng Ren; Chenjun Xiao; Dale Schuurmans; Bo Dai 001 (ICML, 2024)

Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration. - Hongming Zhang 003; Chenjun Xiao; Chao Gao; Han Wang; Bo Xu 002; Martin Müller 003 (NeurIPS, 2024)

Iteratively Refined Behavior Regularization for Offline Reinforcement Learning. - Yi Ma 005; Jianye Hao; Xiaohan Hu; Yan Zheng 002; Chenjun Xiao (NeurIPS, 2024)

Diffusion Spectral Representation for Reinforcement Learning. - Dmitry Shribak; Chen-Xiao Gao; Yitong Li; Chenjun Xiao; Bo Dai 001 (NeurIPS, 2024)

Hindsight Preference Learning for Offline Preference-based Reinforcement Learning. - Chen-Xiao Gao; Shengjun Fang; Chenjun Xiao; Yang Yu 001; Zongzhang Zhang (CoRR, 2024)

β-DQN: Improving Deep Q-Learning By Evolving the Behavior. - Hongming Zhang 003; Fengshuo Bai; Chenjun Xiao; Chao Gao; Bo Xu 002; Martin Müller 003 (AAMAS, 2025)

An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models. - Yangchen Pan; Junfeng Wen; Chenjun Xiao; Philip H. S. Torr (J. Artif. Intell. Res., 2025)

Kimi k1.5: Scaling Reinforcement Learning with LLMs. - Kimi Team; Angang Du; Bofei Gao; Bowei Xing; Changjiu Jiang; Cheng Chen; Cheng Li; Chenjun Xiao; Chenzhuang Du; Chonghua Liao; Chuning Tang; Congcong Wang; et al. (CoRR, 2025)

Large Language Model-Enhanced Multi-Armed Bandits. - Jiahang Sun; Zhiyong Wang; Runhan Yang; Chenjun Xiao; John C. S. Lui; Zhongxiang Dai (CoRR, 2025)

Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning. - Chen-Xiao Gao; Chenyang Wu 001; Mingjun Cao; Chenjun Xiao; Yang Yu 001; Zongzhang Zhang (CoRR, 2025)

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning. - Haiming Wang; Mert Unsal; Xiaohan Lin; Mantas Baksys; Junqi Liu; Marco Dos Santos; Flood Sung; Marina Vinyes; Zhenzhe Ying; Zekai Zhu; Jianqiao Lu; Hugues de Saxcé; et al. (CoRR, 2025)

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies. - Yi Ma 005; Hongyao Tang; Chenjun Xiao; Yaodong Yang 001; Wei Wei 018; Jianye Hao; Jiye Liang (CoRR, 2025)