Chris Junchi Li
According to our database1,
Chris Junchi Li
authored at least 29 papers
between 2016 and 2023.
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Bibliography
2023
Proceedings of the Uncertainty in Artificial Intelligence, 2023
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
Nesterov Meets Optimism: Rate-Optimal Optimistic-Gradient-Based Method for Stochastic Bilinearly-Coupled Minimax Optimization.
CoRR, 2022
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
CoRR, 2022
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium.
CoRR, 2022
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging.
CoRR, 2021
Proceedings of the Conference on Learning Theory, 2021
2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration.
Proceedings of the Conference on Learning Theory, 2020
2019
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory.
Proceedings of the 36th International Conference on Machine Learning, 2019
On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019
2018
Math. Program., 2018
Diffusion Approximations for Online Principal Component Estimation and Global Convergence.
CoRR, 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent.
CoRR, 2017
CoRR, 2017
Diffusion Approximations for Online Principal Component Estimation and Global Convergence.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability.
Proceedings of the 34th International Conference on Machine Learning, 2017
2016
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016