* indicates co-first authorship.

Preprints / Work in Progress

  • LLM for data preprocessing.

  • Gradient Residual Connections. [preprint]
    Yangchen Pan, Qizhen Ying, Philip Torr, Bo Liu.

  • Variability measures for risk-averse RL. [preprint]
    Yudong Luo, Yangchen Pan, Jiaqi Tan, Pascal Poupart.

  • …

Selected Refereed Publications

  • Temporal Difference Learning for Diffusion Models. [paper]
    Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu, Junfeng Wen.
    International Conference on Machine Learning (ICML), 2026.

  • An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models. [paper]
    Yangchen Pan *, Junfeng Wen *, Chenjun Xiao, Philip Torr.
    Journal of Artificial Intelligence Research (JAIR), 2025.

  • PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling. [paper]
    Avery Ma, Yangchen Pan, Amir-massoud Farahmand.
    International Conference on Machine Learning (ICML, spotlight), 2025.

  • Label Alignment Regularization for Distribution Shift. [paper]
    Ehsan Imani, Guojun Zhang, Runjia Li, Jun Luo, Pascal Poupart, Philip Torr, Yangchen Pan.
    Journal of Machine Learning Research (JMLR), 2024.

  • Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination. [paper]
    Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu.
    International Conference on Machine Learning (ICML, spotlight), 2024.

  • A Simple Mixture Policy Parameterization for Improving Sample Efficiency of CVaR Optimization. [paper]
    Yudong Luo, Yangchen Pan, Han Wang, Philip Torr, Pascal Poupart.
    Reinforcement Learning Conference (RLC), 2024.

  • Understanding the robustness difference between SGD and adaptive gradient methods. [paper]
    Avery Ma, Yangchen Pan, Amir-massoud Farahmand.
    Transactions on Machine Learning Research (TMLR, featured certification), 2023.

  • An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. [paper]
    Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan.
    Conference on Neural Information Processing Systems (NeurIPS), 2023.

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

  • Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. [paper]
    Samuel Neumann, Sungsu Lim, Ajin Joseph, Yangchen Pan, Adam White, Martha White.
    International Conference on Learning Representations (ICLR), 2023.

  • Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning. [paper]
    Xutong Zhao, Yangchen Pan, Chenjun Xiao, Sarath Chandar, Janarthanan Rajendran.
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023.

  • Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation.[paper]
    Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood.
    Transactions on Machine Learning Research (TMLR, CoLLAs certification), 2023.

  • Understanding and Mitigating the Limitations of Prioritized Experience Replay. [paper]
    Yangchen Pan *, Jincheng Mei *, Amir-massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo.
    Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

  • An Alternate Policy Gradient Estimator for Softmax Policies. [paper]
    Shivam Garg, Samuele Tosatto, Yangchen Pan, Martha White, A Rupam Mahmood.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

  • Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online. [paper]
    Yangchen Pan, Kirby Banman, Martha White.
    International Conference on Learning Representations (ICLR), 2021.

  • An implicit function learning approach for parametric modal regression. [paper]
    Yangchen Pan, Ehsan Imani, Martha White, Amir-massoud Farahmand.
    Conference on Neural Information Processing Systems (NeurIPS), 2020.

  • Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. [paper]
    Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White.
    International Conference on Learning Representations (ICLR), 2020.

  • Frequency-based Search-control in Dyna. [paper]
    Yangchen Pan *, Jincheng Mei *, Amir-massoud Farahmand.
    International Conference on Learning Representations (ICLR), 2020.

  • Hill Climbing on Value Estimates for Search-control in Dyna. [paper]
    Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White.
    International Joint Conference on Artificial Intelligence (IJCAI), 2019.

  • Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains. [paper]
    Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White.
    International Joint Conference on Artificial Intelligence (IJCAI), 2018.

  • Reinforcement learning with function-valued action spaces for partial differential equation control. [paper]
    Yangchen Pan, Amir-massoud Farahmand, Martha White, Saleh Nabi, Piyush Grover, Daniel Nikovski.
    International Conference on Machine Learning (ICML, long talk), 2018.

  • Adapting kernel representations online using submodular maximization. [paper]
    Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White.
    International Conference on Machine Learning (ICML), 2017.

  • Effective sketching methods for value function approximation. [paper]
    Yangchen Pan, Erfan Sadeqi Azer, Martha White.
    Conference on Uncertainty in Artificial Intelligence (UAI), 2017.

  • Accelerated gradient temporal difference learning. [paper]
    Yangchen Pan, Adam White, Martha White.
    AAAI Conference on Artificial Intelligence (AAAI), 2017.

PhD Thesis

Improving Sample Efficiency of Online Temporal Difference Learning. Yangchen Pan.