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math.OC2025
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
Liam Madden, Emiliano Dall'Anese, Stephen Becker
Stochastic gradient descent is one of the most common iterative algorithms used in machine learning and its convergence analysis is a rich area of research. Understanding its conve…
math.OC2024
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
Nicola Bastianello, Liam Madden, Ruggero Carli +1
This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms. The framework is modeled over the different challenges that these algor…