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20242026
most citedConstant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation

1 citations · 1 across the 9 of their papers we have counts for

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stat.ML2026

On the Peril of (Even a Little) Nonstationarity in Satisficing Regret Minimization

Yixuan Zhang, Ruihao Zhu, Qiaomin Xie

Motivated by the principle of satisficing in decision-making, we study satisficing regret guarantees for nonstationary -armed bandits. We show that in the general realizable, pi…

stat.ML2025

A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression

Yixuan Zhang, Dongyan Huo, Yudong Chen +1

Motivated by robust and quantile regression problems, we investigate the stochastic gradient descent (SGD) algorithm for minimizing an objective function that is locally strong…

stat.ML2024

The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize

Dongyan Huo, Yixuan Zhang, Yudong Chen +1

In this work, we investigate stochastic approximation (SA) with Markovian data and nonlinear updates under constant stepsize . Existing work has primarily focused on either i.…

stat.ML2024

Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA

Yixuan Zhang, Dongyan Huo, Yudong Chen +1

Motivated by Q-learning, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize. We focus on two important classes of dynamics: 1) nonsmooth contractiv…

stat.ML2024★ 1 cited

Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation

Yixuan Zhang, Qiaomin Xie

Stochastic Approximation (SA) is a widely used algorithmic approach in various fields, including optimization and reinforcement learning (RL). Among RL algorithms, Q-learning is pa…