Caio Kalil Lauand

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20222026

most citedMarkovian Foundations for Quasi-Stochastic Approximation with Applications to Extremum Seeking Control

5 citations · 8 across the 8 of their papers we have counts for

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math.ST2024

Revisiting Step-Size Assumptions in Stochastic Approximation

Caio Kalil Lauand, Sean Meyn

Many machine learning and optimization algorithms are built upon the framework of stochastic approximation (SA), for which the selection of step-size (or learning rate) ${α_{n}}$ i…

math.ST2023

The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning

Caio Kalil Lauand, Ioannis Kontoyiannis, Sean Meyn

Theory and application of stochastic approximation (SA) have become increasingly relevant due in part to applications in optimization and reinforcement learning. This paper takes a…