Biography

I am currently a Ph.D. student supervised by Martin Müller and Dale Schuurmans. I am also a student researcher in Google Brain.

My research interest is in the area of artificial intelligence and reinforcement learning. I am particularly interested in sequential decision making in complex problems.

Publication

• The In-Sample Softmax for Offline Reinforcement Learning.
  Chenjun Xiao*, Han Wang*, Yangchen Pan, Adam White and Martha White.
  International Conference on Learning Representations (ICLR), Spotlight, 2023.
  

  [OpenReview]

• Latent Variable Representation for Reinforcement Learning.
  Tongzheng Ren*, Chenjun Xiao*, Tianjun Zhang, Na li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans and Bo Dai.
  International Conference on Learning Representations (ICLR), 2023.
  

  [OpenReview]

• Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experi- ence Replay
  Hongming Zhang, Chenjun Xiao, Han Wang, Jun Jin, Bo Xu, Martin Mueller.
  International Conference on Learning Representations (ICLR), 2023.
  

  [OpenReview]

• Understanding and Leveraging Overparameterization in Recursive Value Estimation.
  Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar Ramirez, Ramki Gummadi, Chris Harris, and Dale Schuurmans.
  International Conference on Learning Representations (ICLR), 2022.
  

  [OpenReview]

• The Curse of Passive Data Collection in Batch ReinforcementLearning.
  Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans and Csaba Szepesvári.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  [arXiv]

• Understanding the Effect of Stochasticity in Policy Optimization.
  Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári†, and Dale Schuurmans†.
  Advances in Neural Information Processing Systems (NeurIPS), 2021.
  

  [arXiv][OpenReview]

• On the Optimality of Batch Policy Optimization Algorithms.
  Chenjun Xiao*, Yifan Wu*, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, and Dale Schuurmans.
  International Conference on Machine Learning (ICML), 2021.
  

  [arXiv]

• Escaping the Gravitational Pull of Softmax.
  Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, and Dale Schuurmans.
  Advances in Neural Information Processing Systems (NeurIPS), Oral Presentation, 2020.
  

  [Paper]

• On the Global Convergence Rates of Softmax Policy Gradient Methods.
  Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, and Dale Schuurmans.
  International Conference on Machine Learning (ICML), 2020.
  

  [arXiv]

• Learning to Combat Compounding-Error in Model-Based Reinforcement Learning.
  Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Mueller.
  NeurIPS 2019 Deep Reinforcement Learning Workshop.
  

  [Paper]

• Maximum Entropy Monte-Carlo Planning.
  Chenjun Xiao, Jincheng Mei, Ruitong Huang, Dale Schuurmans, Martin Müller.
  Advances in Neural Information Processing Systems (NeurIPS), 2019.
  

  [Paper][Poster]

• On Principled Entropy Exploration in Policy Optimization.
  Jincheng Mei, Chenjun Xiao, Ruitong Huang, Dale Schuurmans, Martin Müller.
  International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  

  [Paper][Long version]

• Memory-Augmented Monte Carlo Tree Search,
  Chenjun Xiao, Jincheng Mei, Martin Müller.
  AAAI Conference on Artificial Intelligence (AAAI), 2018.
  Outstanding Paper Award
  

  [Paper]

• Factorization Ranking Model for Move Prediction in the Game of Go,
  Chenjun Xiao and Martin Müller.
  AAAI Conference on Artificial Intelligence (AAAI), 2016.
  

  [Paper]

• Only-One-Victor Pattern Learning in Computer Go,
  Jiao Wang, Chenjun Xiao, Tan Zhu, Chu-Husan Hsueh, Wen-Jie Tseng and I-Chen Wu.
  IEEE Transactions on Computational Intelligence and AI in Games, 2017.
  

  [Paper]

Other Links

Last Modified: 2020-11-05