2 papers
math.PR2026
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
Shubhada Agrawal, Siva Theja Maguluri, Martin Zubeldia
We establish maximal concentration bounds for the iterates generated by stochastic approximation algorithms with general step sizes, where the noise has a finite-state Markovian co…
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…