Chris Junchi Li
16 papers · 2016–2023 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (28) π Interdisciplinary Bridge Β· Bronze π Academic Marathon Β· Sprint π£ Hot Topic Early Bird π Conference Polyglot Β· Silver
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Deep Specialist
(10)
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Century Club Β· Bronze
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Keyword Collector
(86)
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Unstoppable Β· Bronze
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Research topics
Machine Learning
(15)
Optimization & Theory Β· Machine Learning
(15)
Mathematics & Optimization
(10)
Optimization
(9)
Artificial Intelligence
(4)
Core AI
(4)
Core Methods
(4)
Deep Learning
(2)
Reinforcement Learning
(2)
Learning Types
(2)
Methods
(2)
Optimization & Theory Β· Deep Learning
(2)
Applications
(1)
Probability
(1)
Keywords
stochastic gradient descent
(8)
non-convex optimization
(6)
stochastic optimization
(3)
online learning
(2)
diffusion approximation
(2)
minimax optimization
(2)
nesterov acceleration
(2)
stochastic approximation
(2)
regret bound
(2)
reinforcement learning
(2)
nash equilibrium
(2)
independent component analysis
(2)
complexity bound
(2)
function approximation
(2)
finite-sample error bound
(2)
principal component analysis
(1)
temporal difference learning
(1)
variational inequality
(1)
sample efficiency
(1)
representation learning
(1)
Papers
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
ICLR 2023
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
ICML 2023
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems
UAI 2023
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure
NeurIPS 2023
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium
NeurIPS 2022
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
AISTATS 2022
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
COLT 2022
Stochastic Approximation for Online Tensorial Independent Component Analysis
COLT 2021
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration
COLT 2020
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
ICML 2019
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent
NeurIPS 2019
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
NeurIPS 2018
Statistical Sparse Online Regression: A Diffusion Approximation Perspective
AISTATS 2018
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
NeurIPS 2017
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
ICML 2017
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
NeurIPS 2016