Pan Xu

Research Interests

Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics

Bio

My research is centered around Machine Learning, with broad interests in the areas of Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, High Dimensional Statistics, and their applications to real-world problems including Bioinformatics and Healthcare. My research goal is to develop computationally- and data-efficient machine learning algorithms with both strong empirical performance and theoretical guarantees.

Education

• Ph.D. University of California, Los Angeles, 2021

Trainings & Certifications

• Postdoctoral Fellow, California Institute of Technology 2021 - 2022 (2021 - 2022) California Institute of Technology

Positions

• Assistant Professor of Biostatistics & Bioinformatics
• Assistant Professor of Computer Science
• Assistant Professor in the Department of Electrical and Computer Engineering

Courses Taught

• ECE 391: Projects in Electrical and Computer Engineering
• COMPSCI 393: Research Independent Study
• COMPSCI 391: Independent Study
• BIOSTAT 825: Foundation of Reinforcement Learning

Publications

• Liu Z, Yang Y, Wang R, Xu P, Zhou D. How to Provably Improve Return Conditioned Supervised Learning? 2025.
• Liu Z, Xu P. Linear Mixture Distributionally Robust Markov Decision Processes. 2025.
• Tang C, Liu Z, Xu P. Robust Offline Reinforcement Learning with Linearly Structuredn $f$-Divergence Regularization. 2024.
• Wang R, Yang Y, Liu Z, Zhou D, Xu P. Return Augmented Decision Transformer for Off-Dynamics Reinforcementn Learning. 2024.
• Liu Z, Wang W, Xu P. Upper and Lower Bounds for Distributionally Robust Off-Dynamicsn Reinforcement Learning. 2024.
• Xu P. Efficient and robust sequential decision making algorithms. AI Magazine. 2024 Sep 1;45(3):376u201385.
• Yang Y, Xu P. Pre-trained Language Models Improve the Few-shot Prompt Ability ofn Decision Transformer. 2024.
• Ren X, Jin T, Xu P. Optimal Batched Linear Bandits. 2024.
• Lopez VK, Cramer EY, Pagano R, Drake JM, Ou2019Dea EB, Adee M, et al. Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput Biol. 2024 May;20(5):e1011200.
• Hsu H-L, Wang W, Pajic M, Xu P. Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. 2024.
• Jin T, Hsu HL, Chang W, Xu P. Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. In: Proceedings of the Aaai Conference on Artificial Intelligence. 2024. p. 12956u201364.
• Liu Z, Xu P. Minimax Optimal and Computationally Efficient Algorithms forn Distributionally Robust Offline Reinforcement Learning. 2024.
• Liu Z, Xu P. Distributionally Robust Off-Dynamics Reinforcement Learning: Provablen Efficiency with Linear Function Approximation. 2024.
• Shen Y, Xu P, Zavlanos MM. Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits. Transactions on Machine Learning Research. 2024 Jan 1;2024.
• Jin T, Yang Y, Tang J, Xiao X, Xu P. Optimal Batched Best Arm Identification. In: Advances in Neural Information Processing Systems. 2024.
• Hsu HL, Wang W, Pajic M, Xu P. Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. In: Advances in Neural Information Processing Systems. 2024.
• Ren X, Jin T, Xu P. Optimal Batched Linear Bandits. In: Proceedings of Machine Learning Research. 2024. p. 42391u2013416.
• Liu Z, Xu P. Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning. In: Advances in Neural Information Processing Systems. 2024.
• Guo Y, Wang Y, Shi Y, Xu P, Liu A. Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation. In: Advances in Neural Information Processing Systems. 2024.
• Ishfaq H, Lan Q, Xu P, Mahmood AR, Precup D, Anandkumar A, et al. PROVABLE AND PRACTICAL: EFFICIENT EXPLORATION IN REINFORCEMENT LEARNING VIA LANGEVIN MONTE CARLO. In: 12th International Conference on Learning Representations Iclr 2024. 2024.
• Liu Z, Xu P. Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation. In: Proceedings of Machine Learning Research. 2024. p. 2719u201327.
• Deng Y, Zhang R, Xu P, Ma J, Gu Q. Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning. Transactions on Machine Learning Research. 2024 Jan 1;2024.
• Jin T, Hsu H-L, Chang W, Xu P. Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Samplingn on Sparse Hypergraphs. 2023.
• Qin Z, Liu Z, Xu P. Convergence of Sign-based Random Reshuffling Algorithms for Nonconvexn Optimization. 2023.
• Jin T, Yang Y, Tang J, Xiao X, Xu P. Optimal Batched Best Arm Identification. 2023.
• Shen Y, Xu P, Zavlanos MM. Wasserstein Distributionally Robust Policy Evaluation and Learning forn Contextual Bandits. 2023.
• Zhang Y, Qu G, Xu P, Lin Y, Chen Z, Wierman A. Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. Performance Evaluation Review. 2023 Jun 19;51(1):83u20134.
• Zhang Y, Qu G, Xu P, Lin Y, Chen Z, Wierman A. Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. In: Sigmetrics 2023 Abstract Proceedings of the 2023 ACM Sigmetrics International Conference on Measurement and Modeling of Computer Systems. 2023. p. 83u20134.
• Queerinai OO, Ovalle A, Subramonian A, Singh A, Voelcker C, Sutherland DJ, et al. Queer In AI: A Case Study in Community-Led Participatory AI. In: ACM International Conference Proceeding Series. 2023. p. 1882u201395.
• Shea K, Borchering RK, Probert WJM, Howerton E, Bogich TL, Li S-L, et al. Multiple models for outbreak decision support in the face of uncertainty. Proc Natl Acad Sci U S A. 2023 May 2;120(18):e2207537120.
• Zhang Y, Qu G, Xu P, Lin Y, Chen Z, Wierman A. Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 2023 Feb 28;7(1).
• Jin T, Yang X, Xiao X, Xu P. Thompson Sampling with Less Exploration is Fast and Optimal. In: Proceedings of Machine Learning Research. 2023. p. 15239u201361.
• Yang Z, Guo Y, Xu P, Liu A, Anandkumar A. Distributionally Robust Policy Gradient for Offline Contextual Bandits. In: Proceedings of Machine Learning Research. 2023. p. 6443u201362.
• Cramer EY, Huang Y, Wang Y, Ray EL, Cornell M, Bracher J, et al. The United States COVID-19 Forecast Hub dataset. Sci Data. 2022 Aug 1;9(1):462.
• Xu P, Zheng H, Mazumdar E, Azizzadenesheli K, Anandkumar A. Langevin Monte Carlo for Contextual Bandits. In PMLR; 2022.
• Jin T, Xu P, Xiao X, Anandkumar A. Finite-Time Regret of Thompson Sampling Algorithms for Exponentialn Family Multi-Armed Bandits. 2022.
• Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A. 2022 Apr 12;119(15):e2113561119.
• Jin T, Xu P, Xiao X, Anandkumar A. Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. In: Advances in Neural Information Processing Systems. 2022.
• Xu P, Zheng H, Mazumdar E, Azizzadenesheli K, Anandkumar A. Langevin Monte Carlo for Contextual Bandits. In: Proceedings of Machine Learning Research. 2022. p. 24830u201350.
• Wu Y, Jin T, Lou H, Xu P, Farnoud F, Gu Q. Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. In: Proceedings of Machine Learning Research. 2022. p. 11014u201336.
• Lou H, Jin T, Wu Y, Xu P, Gu Q, Farnoud F. Active Ranking without Strong Stochastic Transitivity. In: Advances in Neural Information Processing Systems. 2022.
• Wu Y, Jin T, Lou H, Xu P, Farnoud F, Gu Q. Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwisen Comparisons. 2021.
• Zou D, Xu P, Gu Q. Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. In: Proceedings of Machine Learning Research. 2021. p. 1152u201362.
• Cramer E, Huang Y, Wang Y, Ray E, Cornell M, Bracher J, et al. The United States COVID-19 Forecast Hub dataset. medRxiv. 2021.
• Zou D, Xu P, Gu Q. Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. In: 37th Conference on Uncertainty in Artificial Intelligence Uai 2021. 2021. p. 1152u201362.
• Jin T, Xu P, Shi J, Xiao X, Gu Q. MOTS: Minimax Optimal Thompson Sampling. In: Proceedings of Machine Learning Research. 2021. p. 5074u201383.
• Cramer E, Ray E, Lopez V, Bracher J, Brennen A, Castrou00a7Rivadeneira A, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. medRxiv. 2021.
• Jin T, Tang J, Xu P, Huang K, Xiao X, Gu Q. Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. In: Proceedings of Machine Learning Research. 2021. p. 5065u201373.
• Xu P, Wen Z, Zhao H, Gu Q. Neural Contextual Bandits with Deep Representation and Shallown Exploration. 2020.
• Shea K, Borchering RK, Probert WJM, Howerton E, Bogich TL, Li S, et al. COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support. Cold Spring Harbor Laboratory. 2020.
• Wu Y, Zhang W, Xu P, Gu Q. A Finite Time Analysis of Two Time-Scale Actor Critic Methods. 2020.
• Zhou D, Xu P, Gu Q. Stochastic nested variance reduction for nonconvex optimization. Journal of Machine Learning Research. 2020 May 1;21.
• Jin T, Xu P, Gu Q, Farnoud F. Rank aggregation via heterogeneous thurstone preference models. In: Aaai 2020 34th Aaai Conference on Artificial Intelligence. 2020. p. 4353u201360.
• Xu P, Gu Q. A finite-time analysis of Q-Learning with neural network function approximation. In: 37th International Conference on Machine Learning Icml 2020. 2020. p. 10486u201396.
• Wu Y, Zhang W, Xu P, Gu Q. A finite-time analysis of two time-scale actor-critic methods. In: Advances in Neural Information Processing Systems. 2020.
• Xu P, Gao F, Gu Q. SAMPLE EFFICIENT POLICY GRADIENT METHODS WITH RECURSIVE VARIANCE REDUCTION. In: 8th International Conference on Learning Representations Iclr 2020. 2020.
• Ray E, Wattanachit N, Niemi J, Kanji AH, House K, Cramer E, et al. Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. medRxiv. 2020.
• Zou D, Wang L, Xu P, Chen J, Zhang W, Gu Q. Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States. medRxiv. 2020.
• Jin T, Xu P, Gu Q, Farnoud F. Rank Aggregation via Heterogeneous Thurstone Preference Models. 2019.
• Xu P, Gao F, Gu Q. Sample Efficient Policy Gradient Methods with Recursive Variancen Reduction. 2019.
• Zhou D, Xu P, Gu Q. Stochastic variance-reduced cubic regularization methods. Journal of Machine Learning Research. 2019 Aug 1;20.
• Xu P, Gao F, Gu Q. An Improved Convergence Analysis of Stochastic Variance-Reduced Policyn Gradient. 2019.
• Zou D, Xu P, Gu Q. Stochastic gradient hamiltonian monte carlo methods with recursive variance reduction. In: Advances in Neural Information Processing Systems. 2019.
• Xu P, Gao F, Gu Q. An improved convergence analysis of stochastic variance-reduced policy gradient. In: 35th Conference on Uncertainty in Artificial Intelligence Uai 2019. 2019.
• Zou D, Xu P, Gu Q. Sampling from non-log-concave distributions via stochastic variance-reduced gradient Langevin dynamics. In: Aistats 2019 22nd International Conference on Artificial Intelligence and Statistics. 2019.
• Xu P, Gao F, Gu Q. An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. In: Proceedings of Machine Learning Research. 2019. p. 541u201351.
• Zhou D, Xu P, Gu Q. Finding Local Minima via Stochastic Nested Variance Reduction. 2018.
• Zhou D, Xu P, Gu Q. Stochastic Nested Variance Reduction for Nonconvex Optimization. 2018.
• Zhou D, Xu P, Gu Q. Stochastic Variance-Reduced Cubic Regularized Newton Method. 2018.
• Zou D, Xu P, Gu Q. Stochastic Variance-Reduced Hamilton Monte Carlo Methods. 2018.
• Yu Y, Xu P, Gu Q. Third-order smoothness helps: Faster stochastic optimization algorithms for finding local minima. In: Advances in Neural Information Processing Systems. 2018. p. 4525u201335.
• Zhou D, Xu P, Gu Q. Stochastic nested variance reduction for nonconvex optimization. In: Advances in Neural Information Processing Systems. 2018. p. 3921u201332.
• Xu P, Wang T, Gu Q. Accelerated stochastic mirror descent: From continuous-time dynamics to discrete-time algorithms. In: International Conference on Artificial Intelligence and Statistics Aistats 2018. 2018. p. 1087u201396.
• Xu P, Zou D, Chen J, Gu Q. Global convergence of Langevin dynamics based algorithms for nonconvex optimization. In: Advances in Neural Information Processing Systems. 2018. p. 3122u201333.
• Zou D, Xu P, Gu Q. Subsampled stochastic variance-reduced gradient langevin dynamics. In: 34th Conference on Uncertainty in Artificial Intelligence 2018 Uai 2018. 2018. p. 508u201318.
• Zhou D, Xu P, Gu Q. Stochastic variance-reduced cubic regularized Newton method. In: 35th International Conference on Machine Learning Icml 2018. 2018. p. 9597u2013606.
• Chen J, Xu P, Wang L, Ma J, Gu Q. Covariate adjusted precision matrix estimation via nonconvex optimization. In: 35th International Conference on Machine Learning Icml 2018. 2018. p. 1464u201389.
• Zou D, Xu P, Gu Q. Stochastic variance-reduced Hamilton Monte Carlo methods. In: 35th International Conference on Machine Learning Icml 2018. 2018. p. 9647u201356.
• Xu P, Wang T, Gu Q. Continuous and discrete-time accelerated stochastic mirror descent for strongly convex functions. In: 35th International Conference on Machine Learning Icml 2018. 2018. p. 8738u201351.
• Yu Y, Xu P, Gu Q. Third-order Smoothness Helps: Even Faster Stochastic Optimizationn Algorithms for Finding Local Minima. 2017.
• Xu P, Chen J, Zou D, Gu Q. Global Convergence of Langevin Dynamics Based Algorithms for Nonconvexn Optimization. 2017.
• Xu P, Ma J, Gu Q. Speeding Up Latent Variable Gaussian Graphical Model Estimation vian Nonconvex Optimizations. 2017.
• Xu P, Ma J, Gu Q. Speeding up latent variable Gaussian graphical model estimation via nonconvex optimization. In: Advances in Neural Information Processing Systems. 2017. p. 1934u201345.
• Xu P, Zhang T, Gu Q. Efficient algorithm for sparse tensor-variate gaussian graphical models via gradient descent. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics Aistats 2017. 2017.
• Chaudhry A, Xu P, Gu Q. Uncertainty assessment and false discovery rate control in high-dimensional Granger causal inference. In: 34th International Conference on Machine Learning Icml 2017. 2017. p. 1120u201355.
• Xu P, Tian L, Gu Q. Communication-efficient Distributed Estimation and Inference forn Transelliptical Graphical Models. 2016.
• Xu P, Gu Q. Semiparametric differential graph models. In: Advances in Neural Information Processing Systems. 2016. p. 1072u201380.
• Tian L, Xu P, Gu Q. Forward backward greedy algorithms for multi-task learning with faster rates. In: 32nd Conference on Uncertainty in Artificial Intelligence 2016 Uai 2016. 2016. p. 735u201344.
• Xu P, Wen Z, Zhao H, Gu Q. Neural Contextual Bandits with Deep Representation and Shallow Exploration. In.
• Jin T, Xu P, Xiao X, Gu Q. Double Explore-then-Commit: Asymptotic Optimality and Beyond. In: Proceedings of Thirty Fourth Conference on Learning Theory. PMLR;