Pan Xu
Orcid: 0000-0002-2559-8622Affiliations:
- University of California, Los Angeles, Department of Computer Science, CA, USA
- University of Virginia, Department of Systems andInformation Engineering, Charlottesville, VA, USA
According to our database1,
Pan Xu
authored at least 44 papers
between 2016 and 2024.
Collaborative distances:
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Bibliography
2024
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.
Proc. ACM Meas. Anal. Comput. Syst., March, 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo.
CoRR, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023
2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Proceedings of the Conference on Learning Theory, 2021
2020
J. Mach. Learn. Res., 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 37th International Conference on Machine Learning, 2020
Proceedings of the 8th International Conference on Learning Representations, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
2019
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
2018
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions.
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima.
CoRR, 2017
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations.
CoRR, 2017
CoRR, 2017
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference.
Proceedings of the 34th International Conference on Machine Learning, 2017
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
2016
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016