148. On Almost Sure Convergence Rates for Stochastic Gradient Methods
Invited abstract in session WF-6: Stochastic Gradient Methods: Bridging Theory and Practice, stream Challenges in nonlinear programming.
Wednesday, 16:20 - 18:00Room: M:H
Authors (first author is the speaker)
| 1. | Sara Klein
|
| Mathematics Institute, University of Mannheim |
Abstract
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow for simple analysis, they are often not satisfied in applications. In recent years, global and local gradient domination properties have been shown to be a sufficient relaxation of strong convexity. They have been proven to hold in diverse settings, such as policy gradient methods. In this talk, we will discuss almost sure convergence rates for SGD (with and without momentum) under global and local gradient domination assumptions. Afterwards, we will apply the results to reinforcement learning, more precisely to softmax parameterized stochastic policy gradient methods.This is joint work with Simon Weissmann and Leif Döring.
Keywords
- Artificial intelligence based optimization methods and appl
- Optimization for learning and data analysis
Status: accepted
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