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ORCIDPan Xu 0002
Person information
- affiliation: University of California, Los Angeles, Department of Computer Science, CA, USA
- affiliation: University of Virginia, Department of Systems andInformation Engineering, Charlottesville, VA, USA
Other persons with the same name
- Pan Xu — disambiguation page
- Pan Xu 0001
![[0000-0002-6362-6727]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-6362-6727")
— New Jersey Institute of Technology (NJIT), Department of Computer Science, Newark, NJ, USA (and 2 more) - Pan Xu 0003 — Tongji University, School of Electronics and Information Engineering, Shanghai, China
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2020 – today
- 2024
[c34]Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu:
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. AAAI 2024: 12956-12964- 2023
- [j3]Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman:
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. Proc. ACM Meas. Anal. Comput. Syst. 7(1): 13:1-13:51 (2023) - [c33]Organizers Of QueerInAI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. FAccT 2023: 1882-1895 - [c32]Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu:
Thompson Sampling with Less Exploration is Fast and Optimal. ICML 2023: 15239-15261 - [c31]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: 83-84 - [i25]Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. CoRR abs/2303.16972 (2023) - [i24]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 abs/2305.18246 (2023) - [i23]Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu:
Optimal Batched Best Arm Identification. CoRR abs/2310.14129 (2023) - [i22]Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu:
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. CoRR abs/2312.15549 (2023) - 2022
- [c30]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. AISTATS 2022: 11014-11036 - [c29]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. ICLR 2022 - [c28]Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar:
Langevin Monte Carlo for Contextual Bandits. ICML 2022: 24830-24850 - [c27]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar:
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. NeurIPS 2022 - [c26]Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud:
Active Ranking without Strong Stochastic Transitivity. NeurIPS 2022 - [i21]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar:
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. CoRR abs/2206.03520 (2022) - [i20]Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar:
Langevin Monte Carlo for Contextual Bandits. CoRR abs/2206.11254 (2022) - [i19]Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman:
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. CoRR abs/2211.17116 (2022) - 2021
- [c25]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. COLT 2021: 2584-2633 - [c24]Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu:
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. ICML 2021: 5065-5073 - [c23]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. ICML 2021: 5074-5083 - [c22]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI 2021: 1152-1162 - [i18]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. CoRR abs/2110.04136 (2021) - 2020
- [j2]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21: 103:1-103:63 (2020) - [c21]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. AAAI 2020: 4353-4360 - [c20]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. ICLR 2020 - [c19]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. ICML 2020: 10555-10565 - [c18]Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. NeurIPS 2020 - [i17]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. CoRR abs/2002.09174 (2020) - [i16]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. CoRR abs/2003.01803 (2020) - [i15]Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite Time Analysis of Two Time-Scale Actor Critic Methods. CoRR abs/2005.01350 (2020) - [i14]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR abs/2010.09597 (2020) - [i13]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. CoRR abs/2012.01780 (2020)
2010 – 2019
- 2019
- [j1]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20: 134:1-134:47 (2019) - [c17]Difan Zou, Pan Xu, Quanquan Gu:
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS 2019: 2936-2945 - [c16]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS 2019: 3830-3841 - [c15]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. UAI 2019: 541-551 - [i12]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. CoRR abs/1905.12615 (2019) - [i11]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. CoRR abs/1909.08610 (2019) - [i10]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. CoRR abs/1912.01211 (2019) - [i9]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. CoRR abs/1912.04511 (2019) - 2018
- [c14]Pan Xu, Tianhao Wang, Quanquan Gu:
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. AISTATS 2018: 1087-1096 - [c13]Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu:
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML 2018: 921-930 - [c12]Pan Xu, Tianhao Wang, Quanquan Gu:
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. ICML 2018: 5488-5497 - [c11]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML 2018: 5985-5994 - [c10]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML 2018: 6023-6032 - [c9]Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS 2018: 3126-3137 - [c8]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS 2018: 3925-3936 - [c7]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS 2018: 4530-4540 - [c6]Difan Zou, Pan Xu, Quanquan Gu:
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI 2018: 508-518 - [i8]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR abs/1802.04791 (2018) - [i7]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR abs/1802.04796 (2018) - [i6]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR abs/1806.07811 (2018) - [i5]Dongruo Zhou, Pan Xu, Quanquan Gu:
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR abs/1806.08782 (2018) - [i4]Dongruo Zhou, Pan Xu, Quanquan Gu:
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR abs/1811.11989 (2018) - 2017
- [c5]Pan Xu, Tingting Zhang, Quanquan Gu:
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. AISTATS 2017: 923-932 - [c4]Aditya Chaudhry, Pan Xu, Quanquan Gu:
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. ICML 2017: 684-693 - [c3]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. NIPS 2017: 1933-1944 - [i3]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. CoRR abs/1702.08651 (2017) - [i2]Pan Xu, Jinghui Chen, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. CoRR abs/1707.06618 (2017) - [i1]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR abs/1712.06585 (2017) - 2016
- [c2]Pan Xu, Quanquan Gu:
Semiparametric Differential Graph Models. NIPS 2016: 1064-1072 - [c1]Lu Tian, Pan Xu, Quanquan Gu:
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. UAI 2016
Coauthor Index
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