4 papers
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
Kaicheng Jin, Yang Peng, Jiansheng Yang +1
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The purpose of distributional TD…
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
Yang Peng, Kaicheng Jin, Liangyu Zhang +1
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The aim of distributional TD lea…
A Regularized Online Newton Method for Stochastic Convex Bandits with Linear Vanishing Noise
Jingxin Zhan, Yuchen Xin, Kaicheng Jin +1
We study a stochastic convex bandit problem where the subgaussian noise parameter is assumed to decrease linearly as the learner selects actions closer and closer to the minimizer…
Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
Chuhan Xie, Kaicheng Jin, Jiadong Liang +1
We study time-uniform statistical inference for parameters in stochastic approximation (SA), which encompasses a bunch of applications in optimization and machine learning. To that…