1 citations · 2 across the 4 of their papers we have counts for
5 papers
Finite-Time Analysis of Projected Two-Time-Scale Stochastic Approximation
Yitao Bai, Thinh T. Doan, Justin Romberg
We study the finite-time convergence of projected linear two-time-scale stochastic approximation with constant step sizes and Polyak--Ruppert averaging. We establish an explicit me…
A general technique for approximating high-dimensional empirical kernel matrices
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
We present simple, user-friendly bounds for the expected operator norm of a random kernel matrix under general conditions on the kernel function . Our approach uses…
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation
Yitao Bai, Sihan Zeng, Justin Romberg +1
We study policy evaluation problems in multi-task reinforcement learning (RL) under a low-rank representation setting. In this setting, we are given learning tasks where the co…
Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
Chiraag Kaushik, Justin Romberg, Vidya Muthukumar
The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares pr…
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Sihan Zeng, Thinh T. Doan, Justin Romberg
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves multiple tasks at the same time. This paper presents a constrained formulation for multi…