42 citations · 268 across the 21 of their papers we have counts for
27 papers · 1 filter
Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails
Ruinan Jin, Yingbin Liang, Shaofeng Zou
Despite Adam demonstrating faster empirical convergence than SGD in many applications, much of the existing theory yields guarantees essentially comparable to those of SGD, leaving…
Data Sampling Affects the Complexity of Online SGD over Dependent Data
Shaocong Ma, Ziyi Chen, Yi Zhou +2
Conventional machine learning applications typically assume that data samples are independently and identically distributed (i.i.d.). However, practical scenarios often involve a d…
A Unified Off-Policy Evaluation Approach for General Value Function
Tengyu Xu, Zhuoran Yang, Zhaoran Wang +1
General Value Function (GVF) is a powerful tool to represent both the {\em predictive} and {\em retrospective} knowledge in reinforcement learning (RL). In practice, often multiple…
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
Tengyu Xu, Zhuoran Yang, Zhaoran Wang +1
Designing off-policy reinforcement learning algorithms is typically a very challenging task, because a desirable iteration update often involves an expectation over an on-policy di…
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms
Tengyu Xu, Yingbin Liang
Two timescale stochastic approximation (SA) has been widely used in value-based reinforcement learning algorithms. In the policy evaluation setting, it can model the linear and non…
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
Tengyu Xu, Yingbin Liang, Guanghui Lan
In safe reinforcement learning (SRL) problems, an agent explores the environment to maximize an expected total reward and meanwhile avoids violation of certain constraints on a num…