82 citations · 546 across the 65 of their papers we have counts for
8 papers · 1 filter
Optimal Algorithms for Convex Nested Stochastic Composite Optimization
Zhe Zhang, Guanghui Lan
Recently, convex nested stochastic composite optimization (NSCO) has received considerable attention for its applications in reinforcement learning and risk-averse optimization. Th…
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning
Georgios Kotsalis, Guanghui Lan, Tianjiao Li
The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evalu…
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…
Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation
Georgios Kotsalis, Guanghui Lan, Tianjiao Li
In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) project…
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan +1
Nonconvex sparse models have received significant attention in high-dimensional machine learning. In this paper, we study a new model consisting of a general convex or nonconvex ob…
Conditional Gradient Methods for Convex Optimization with General Affine and Nonlinear Constraints
Guanghui Lan, Edwin Romeijn, Zhiqiang Zhou
Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of spar…