36 citations · 186 across the 15 of their papers we have counts for
10 papers · 1 filter
Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation
Yue Wang, Shaofeng Zou, Yi Zhou
Temporal-difference learning with gradient correction (TDC) is a two time-scale algorithm for policy evaluation in reinforcement learning. This algorithm was initially proposed wit…
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Shaocong Ma, Ziyi Chen, Yi Zhou +1
Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function appro…
Momentum with Variance Reduction for Nonconvex Composition Optimization
Ziyi Chen, Yi Zhou
Composition optimization is widely-applied in nonconvex machine learning. Various advanced stochastic algorithms that adopt momentum and variance reduction techniques have been dev…
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…
Perception-Distortion Trade-off with Restricted Boltzmann Machines
Chris Cannella, Jie Ding, Mohammadreza Soltani +1
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy functio…