61 citations · 750 across the 66 of their papers we have counts for
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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…
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
Kaiyi Ji, Zhe Wang, Bowen Weng +3
Variance-reduced algorithms, although achieve great theoretical performance, can run slowly in practice due to the periodic gradient estimation with a large batch of data. Batch-si…
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…
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
Tengyu Xu, Shaofeng Zou, Yingbin Liang
Gradient-based temporal difference (GTD) algorithms are widely used in off-policy learning scenarios. Among them, the two time-scale TD with gradient correction (TDC) algorithm has…
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…
Finite-Sample Analysis for SARSA with Linear Function Approximation
Shaofeng Zou, Tengyu Xu, Yingbin Liang
SARSA is an on-policy algorithm to learn a Markov decision process policy in reinforcement learning. We investigate the SARSA algorithm with linear function approximation under the…