@inproceedings{e105e31d468e4684beaae7bde02dcb08,
title = "Rule-enhanced penalized regression by column generation using rectangular maximum agreement",
abstract = "We describe a procedure enhancing Lx-penalized regression by adding dynamically generated rules describing multidimensional {"}box{"} sets. Our rule-adding procedure is based on the classical column generation method for high-dimensional linear programming. The pricing problem for our column generation procedure reduces to the AAP-hard rectangular maximum agreement (RMA) problem of finding a box that best discriminates between two weighted datasets. We solve this problem exactly using a parallel branch-and-bound procedure. The resulting rule-enhanced regression method is computation-intensive, but has promising prediction performance.",
author = "Jonathan Eckstein and Noam Goldberg and Ai Kagawa",
note = "Publisher Copyright: Copyright 2017 by the author(s).; 34th International Conference on Machine Learning, ICML 2017 ; Conference date: 06-08-2017 Through 11-08-2017",
year = "2017",
month = jan,
day = "1",
language = "American English",
series = "34th International Conference on Machine Learning, ICML 2017",
pages = "1762--1770",
booktitle = "34th International Conference on Machine Learning, ICML 2017",
}