Sparse hierarchical regression with polynomials

DOI10.1007/s10994-020-05868-6zbMath1497.68410arXiv1709.10030OpenAlexW3001585632WikidataQ120689980 ScholiaQ120689980MaRDI QIDQ782451

Bart P. G. Van Parys, Dimitris J. Bertsimas

Publication date: 27 July 2020

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.10030

zbMATH Keywords

nonlinear regressionsparse regressioninteger optimizationpolynomial learning

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• LS-SVMlab
• Julia

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Best subset selection via a modern optimization lens
• Statistics for high-dimensional data. Methods, theory and applications.
• Characterization of the equivalence of robustification and regularization in linear and matrix regression
• The composite absolute penalties family for grouped and hierarchical variable selection
• On optimal nonlinear associative recall
• Solving mixed integer nonlinear programs by outer approximation
• On selecting interacting features from high-dimensional data
• Interaction pursuit in high-dimensional multi-response regression via distance correlation
• Sparse high-dimensional regression: exact scalable algorithms and phase transitions
• Branch-and-Price: Column Generation for Solving Huge Integer Programs
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Computing in Operations Research Using Julia
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• An outer-approximation algorithm for a class of mixed-integer nonlinear programs
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• 10.1162/153244303322753616
• Matching pursuits with time-frequency dictionaries
• Interaction Screening for Ultrahigh-Dimensional Data
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: Sparse hierarchical regression with polynomials