Pan Xu

Recent News

[2025/05]
Our papers on distributionally robust MDPs with online interaction and linear robust MDPs with regularizations get accepted to ICML 2025.
- Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
- Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization
[2024/11] I am serving as an action editor for Transactions on Machine Learning Research (TMLR).
[2024/09]
Our papers on pure exploration in batched bandits, randomized exploration in MARL, off-dynamics reinforcement learning, and distributionally robust RL get accepted to NeurIPS 2024.
- Optimal Batched Best Arm Identification
- Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
- Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation
- Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
[2024/09]
My paper which extends my talk at New Faculty Highlights, focused on the efficient exploration and robust learning in decision making, gets published in AI Magazine.
- Efficient and Robust Sequential Decision Making Algorithms
[2024/08]
Our paper on self-supervised training of hypergraph neural networks gets accepted to TMLR.
- PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning
[2024/05]
Our paper on exploration in deep reinforcement learning gets accepted to RLC 2024.
- More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
[2024/05]
Our paper on batched linear bandits gets accepted to ICML 2024.
- Optimal Batched Linear Bandits
[2024/01]
Our paper on distributionally robust reinforcement learning gets accepted to AISTATS 2024.
- Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
[2024/01]
Our paper on exploration in deep reinforcement learning gets accepted to ICLR 2024.
- Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
[2023/12]
Our paper on distributional robust bandits gets accepted to TMLR with a Featured Certification.
- Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
[2023/12] I have been invited to give a presentation at the New Faculty Highlights program at AAAI 2024.
[2023/12]
Our paper on multi-agent multi-armed bandits gets accepted to AAAI 2024 as an Oral Presentation.
- Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs
[2023/08] Received an award from NSF on approximate sampling based exploration for sequential decision making. [NSF award information]
[2023/08] I am serving as an area chair for AISTATS 2024.
[2023/08] I am serving as an area chair for ICLR 2024.
[2023/04]
Our paper on randomized exploration in multi-armed bandits gets accepted to ICML 2023.
- Thompson Sampling with Less Exploration is Fast and Optimal
[2023/04]
Our paper on Participatory AI gets accepted to FAccT 2023 and wins the Best Paper Award.
- Queer In AI: A Case Study in Community-Led Participatory AI
[2023/03] I am serving as an area chair for NeurIPS 2023.
[2023/01] I received the Whitehead Scholar award from the Duke University School of Medicine.
[2023/01]
Our paper on offline contextual bandits gets accepted to AISTATS 2023.
- Distributionally Robust Policy Gradient for Offline Contextual Bandits
[2023/01] I am serving as an area chair for ICML 2023.
[2022/12]
Our paper on multi-agent reinforcement learning gets accepted to ACM SIGMETRICS 2023.
- Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
[2022/09]
Two papers on Thompson sampling and preference learning get accepted to NeurIPS 2022.
- Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits
- Active Ranking without Strong Stochastic Transitivity
[2022/08] I started at Duke University as an assistant professor.
[2022/01] I received the PIMCO Postdoctoral Fellowship in Data Science.
[2021/06] I received the UCLA Outstanding Graduate Student Research Award.
[2021/01] I am selected to the inaugural cohort of Rising Stars in Data Science by the Center for Data and Computing at the University of Chicago.
[2020/09] I am selected as a top reviewer for the International Conference on Machine Learning (ICML) 2020.
[2020/05] Our forecast on Combating COVID-19 has been adopted in the COVID-19 Forecasts by Centers for Disease Control and Prevention (CDC), California COVID Assessment Tool (CalCat) by California Department of Public Health (CDPH), and the COVID-19 Forecast Hub by the Reich Lab of the University of Massachusetts Amherst.
[2020/03] We launched the project Combating COVID-19 to model and predict the spread of coronavirus using machine learning empowered epidemic models. Details can be found at covid19.uclaml.org
[2020/02] One paper (P1) on rank aggregation is selected as Oral Presentation in AAAI 2020.
[2019/09] I am selected as a top reviewer for the Conference on Advances in Neural Information Processing Systems (NeurIPS) 2019.
[2019/06] One paper (P1) on reinforcement learning is selected as Oral Presentation in UAI 2019!
[2019/05] I gave a tutorial on "Nonconvex Optimization for Knowledge Discovery and Data Mining" with Quanquan Gu and Zhaoran Wang in SIAM International Conference on Data Mining (SDM'19). Slides can be found at [Slides]
[2018/09] Two papers (P1, P2) on nonconvex optimization are selected as Spotlight in NeurIPS 2018!
[2018/05] One paper (P1) on high-dimensional statistics is selected as Long Oral Talk in ICML 2018.
[2016/06] I received the Presidential Fellowship in Data Science from the University of Virginia.

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