Max-affine regression via first-order methods
type: Item
instance of: scholarly article
title: Max-affine regression via first-order methods
author: Seonho Kim
author: Kiryung Lee
published in: SIAM Journal on Mathematics of Data Science
publication date: 2024-08-06T00:00:00Z
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7892645
zbMATH Keywords: max-affine regression
zbMATH Keywords: nonconvex optimization
zbMATH Keywords: gradient descent
zbMATH Keywords: stochastic gradient descent
MaRDI profile type: Publication
cites work: Optimization Methods for Large-Scale Machine Learning
cites work: Q4821526
cites work: Generalized Gradients and Applications
cites work: 10.1162/15324430260185628
cites work: Statistical mechanics of learning
cites work: Max-Affine Regression: Parameter Estimation for Gaussian Designs
cites work: Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup*
cites work: Q5270493
cites work: Covering Numbers for Convex Functions
cites work: Q2933946
cites work: Q3923954
cites work: Advances and Open Problems in Federated Learning
cites work: Consistency of multidimensional convex regression
cites work: Convex piecewise-linear fitting
cites work: Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
cites work: Robust Stochastic Approximation Approach to Stochastic Programming
cites work: Q4626283
cites work: Phase retrieval via randomized Kaczmarz: theoretical guarantees
cites work: Fitting piecewise linear continuous functions
cites work: High-Dimensional Probability
cites work: Phase Retrieval via Reweighted Amplitude Flow
cites work: Q4637073
DOI: 10.1137/23M1594662
description: scientific article; zbMATH DE number 7892645