4 papers
Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance
Hongxu Chen, Ke Wei, Luo Luo
Decentralized optimization is critical for solving large-scale machine learning problems over distributed networks, where multiple nodes collaborate through local communication. In…
Stability and Generalization of Stochastic Nonconvex Optimization under Heavy-Tailed Noise: From Gradient Norm to Goldstein Stationarity
Hongxu Chen, Ke Wei, Xiaoming Yuan +1
The empirical evidence indicates that stochastic optimization with heavy-tailed gradient noise is more appropriate to characterize the training of machine learning models than that…
Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
Wenye Li, Hongxu Chen, Jiacai Liu +1
This paper studies the policy mirror descent (PMD) method, which is a general policy optimization framework in reinforcement learning and can cover a wide range of policy gradient…
A Zeroth-Order Variance-Reduced Method for Decentralized Stochastic Non-convex Optimization
Hongxu Chen, Jinchi Chen, Ke Wei
In this paper, we consider a distributed stochastic non-convex optimization problem, which is about minimizing a sum of local cost functions over a network with only zeroth-ord…