🎓 We are a team of researchers at the Duke University lead by Dr. Pan Xu.
🧐 We do research in machine learning, especially in reinforcement learning, optimization, sampling, and data science.
📄 Find most recent research papers on our website.
📧 Please contact pan.xu@duke.edu for any research discussion.
Code for the paper "Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation", International Conference on Artificial Intelligence and Sta…
Code for the paper "Optimal Batched Linear Bandits", International Conference on Machine Learning (ICML) 2024
Python 3
Code for the paper "Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS) 2024
Python 3
Code for the paper "Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs", AAAI Conference on Artificial Intelligence 2024
Python 2
Code for the paper "Optimal Batched Best Arm Identification", Advances in Neural Information Processing Systems (NeurIPS) 2024
Code for the paper "Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer", Transactions on Machine Learning Research (TMLR) 2025
Code for the paper "Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective"
Code for the Paper "Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization", International Conference on Machine Learning (ICML) 2025
Code for the paper "Optimal Batched Best Arm Identification", Advances in Neural Information Processing Systems (NeurIPS) 2024
Code for the paper "Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS) 2024
Code for the paper "Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS) 2024
The official code release for "More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling", Reinforcement Learning Conference (RLC) 2024
Code for the paper "Optimal Batched Linear Bandits", International Conference on Machine Learning (ICML) 2024
Code for the paper "Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs", AAAI Conference on Artificial Intelligence 2024
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