Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent

DOI10.1007/978-3-319-24486-0_21zbMath1471.68222OpenAlexW2294540259MaRDI QIDQ2835640

Guillaume Papa, Pascal Bianchi, Stéphan Clémençon

Publication date: 30 November 2016

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-24486-0_21

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Stochastic approximation (62L20)

Uses Software

• Saga

Cites Work

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• Minimizing finite sums with the stochastic average gradient
• Weak convergence rates for stochastic approximation with application to multiple targets and simulated annealing
• Introductory lectures on convex optimization. A basic course.
• Robust Stochastic Approximation Approach to Stochastic Programming
• Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
• Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
• A Stochastic Approximation Method
• Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm

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