Stochastic gradient descent: where optimization meets machine learning

DOI10.4171/ICM2022/171OpenAlexW4389775782MaRDI QIDQ6200207

Rachel Ward

Publication date: 22 March 2024

Published in: International Congress of Mathematicians (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4171/icm2022/171

zbMATH Keywords

convergencestochastic approximationsmoothnessadaptive gradient methodstep-size choice

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Optimization problems in solid mechanics (74P99) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

Cites Work

• Unnamed Item
• Unnamed Item
• A randomized Kaczmarz algorithm with exponential convergence
• Error bounds for the method of alternating projections
• The rate of convergence for the method of alternating projections. II
• Robust Stochastic Approximation Approach to Stochastic Programming
• Optimization Methods for Large-Scale Machine Learning
• Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming
• Theory of Reproducing Kernels
• A Stochastic Approximation Method
• Approximation Methods which Converge with Probability one
• Neural tangent kernel: convergence and generalization in neural networks (invited paper)

This page was built for publication: Stochastic gradient descent: where optimization meets machine learning