Pan Xu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 62

Top Topics

Monte Carlo Methods

Variance Reduction

Convex Optimization

Top Venues

Publications

Xuanfei Ren, Tianyuan Jin, Pan Xu
Optimal Batched Linear Bandits. CoRR (2024)
Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. CoRR (2024)
Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. CoRR (2024)
Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. AAAI (2024)
Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR (2024)
Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu
Thompson Sampling with Less Exploration is Fast and Optimal. ICML (2023)
Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu
Optimal Batched Best Arm Identification. CoRR (2023)
Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. SIGMETRICS (Abstracts) (2023)
Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR (2023)
Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. CoRR (2023)
Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud
Active Ranking without Strong Stochastic Transitivity. NeurIPS (2022)
Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. AISTATS (2022)
Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
Neural Contextual Bandits with Deep Representation and Shallow Exploration. ICLR (2022)
Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar
Langevin Monte Carlo for Contextual Bandits. ICML (2022)
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. CoRR (2022)
Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar
Langevin Monte Carlo for Contextual Bandits. CoRR (2022)
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. NeurIPS (2022)
Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. CoRR (2021)
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
Double Explore-then-Commit: Asymptotic Optimality and Beyond. COLT (2021)
Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
MOTS: Minimax Optimal Thompson Sampling. ICML (2021)
Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. ICML (2021)
Difan Zou, Pan Xu, Quanquan Gu
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI (2021)
Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
Neural Contextual Bandits with Deep Representation and Shallow Exploration. CoRR (2020)
Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. NeurIPS (2020)
Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
MOTS: Minimax Optimal Thompson Sampling. CoRR (2020)
Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
A Finite Time Analysis of Two Time-Scale Actor Critic Methods. CoRR (2020)
Pan Xu, Felicia Gao, Quanquan Gu
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. ICLR (2020)
Pan Xu, Quanquan Gu
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. ICML (2020)
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
Double Explore-then-Commit: Asymptotic Optimality and Beyond. CoRR (2020)
Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
Rank Aggregation via Heterogeneous Thurstone Preference Models. AAAI (2020)
Difan Zou, Pan Xu, Quanquan Gu
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR (2020)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21 (2020)
Pan Xu, Felicia Gao, Quanquan Gu
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. CoRR (2019)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20 (2019)
Pan Xu, Quanquan Gu
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. CoRR (2019)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS (2019)
Difan Zou, Pan Xu, Quanquan Gu
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS (2019)
Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
Rank Aggregation via Heterogeneous Thurstone Preference Models. CoRR (2019)
Pan Xu, Felicia Gao, Quanquan Gu
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. CoRR (2019)
Pan Xu, Felicia Gao, Quanquan Gu
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. UAI (2019)
Pan Xu, Tianhao Wang, Quanquan Gu
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. ICML (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS (2018)
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR (2018)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML (2018)
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR (2018)
Pan Xu, Tianhao Wang, Quanquan Gu
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. AISTATS (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR (2018)
Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS (2018)
Yaodong Yu, Pan Xu, Quanquan Gu
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR (2018)
Difan Zou, Pan Xu, Quanquan Gu
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI (2018)
Dongruo Zhou, Pan Xu, Quanquan Gu
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR (2018)
Pan Xu, Tingting Zhang, Quanquan Gu
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. AISTATS (2017)
Pan Xu, Jian Ma, Quanquan Gu
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. NIPS (2017)
Yaodong Yu, Pan Xu, Quanquan Gu
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR (2017)
Pan Xu, Jian Ma, Quanquan Gu
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. CoRR (2017)
Aditya Chaudhry, Pan Xu, Quanquan Gu
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. ICML (2017)
Pan Xu, Jinghui Chen, Quanquan Gu
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. CoRR (2017)
Pan Xu, Quanquan Gu
Semiparametric Differential Graph Models. NIPS (2016)
Lu Tian, Pan Xu, Quanquan Gu
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. UAI (2016)