18 citations · 41 across the 11 of their papers we have counts for
11 papers
Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
Guanghui Lan, Tianjiao Li, Yangyang Xu
We present a novel class of projected gradient (PG) methods for minimizing a smooth but not necessarily convex function over a convex compact set. We first provide a novel analysis…
Auto-conditioned primal-dual hybrid gradient method and alternating direction method of multipliers
Guanghui Lan, Tianjiao Li
Line search procedures are often employed in primal-dual methods for bilinear saddle point problems, especially when the norm of the linear operator is large or difficult to comput…
A simple uniformly optimal method without line search for convex optimization
Tianjiao Li, Guanghui Lan
Line search (or backtracking) procedures have been widely employed into first-order methods for solving convex optimization problems, especially those with unknown problem paramete…
Accelerated stochastic approximation with state-dependent noise
Sasila Ilandarideva, Anatoli Juditsky, Guanghui Lan +1
We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classi…
Stochastic first-order methods for average-reward Markov decision processes
Tianjiao Li, Feiyang Wu, Guanghui Lan
We study average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy optimization and policy evaluatio…
Accelerated and instance-optimal policy evaluation with linear function approximation
Tianjiao Li, Guanghui Lan, Ashwin Pananjady
We study the problem of policy evaluation with linear function approximation and present efficient and practical algorithms that come with strong optimality guarantees. We begin by…
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
Tianjiao Li, Ziwei Guan, Shaofeng Zou +3
The problem of constrained Markov decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraint…
The Multi-Modal Video Reasoning and Analyzing Competition
Haoran Peng, He Huang, Li Xu +15
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. This competition is composed of four differen…
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
Tianjiao Li, Jun Liu, Wei Zhang +3
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large,…
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…
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…