22 citations · 67 across the 5 of their papers we have counts for
5 papers
Reanalysis of Variance Reduced Temporal Difference Learning
Tengyu Xu, Zhe Wang, Yi Zhou +1
Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization…
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization
Kaiyi Ji, Zhe Wang, Yi Zhou +1
Two types of zeroth-order stochastic algorithms have recently been designed for nonconvex optimization respectively based on the first-order techniques SVRG and SARAH/SPIDER. This…
Distributed SGD Generalizes Well Under Asynchrony
Jayanth Regatti, Gaurav Tendolkar, Yi Zhou +2
The performance of fully synchronized distributed systems has faced a bottleneck due to the big data trend, under which asynchronous distributed systems are becoming a major popula…
Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization
Yi Zhou, Zhe Wang, Kaiyi Ji +2
Two new stochastic variance-reduced algorithms named SARAH and SPIDER have been recently proposed, and SPIDER has been shown to achieve a near-optimal gradient oracle complexity fo…
SGD Converges to Global Minimum in Deep Learning via Star-convex Path
Yi Zhou, Junjie Yang, Huishuai Zhang +2
Stochastic gradient descent (SGD) has been found to be surprisingly effective in training a variety of deep neural networks. However, there is still a lack of understanding on how…