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20182026
most citedFederated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning

13 citations · 25 across the 13 of their papers we have counts for

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stat.ML2025

A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging

Sajad Khodadadian, Martin Zubeldia

Polyak-Ruppert averaging is a widely used technique to achieve the optimal asymptotic variance of stochastic approximation (SA) algorithms, yet its high-probability performance gua…