Conditional Linear Regression
Machine Learning
2019-07-11 v2 Data Structures and Algorithms
Machine Learning
Abstract
Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there does not often exist a good model on the whole dataset, so we seek to find a small subset where there exists a useful model. We are interested in finding a linear rule capable of achieving more accurate predictions for just a segment of the population. We give an efficient algorithm with theoretical analysis for the conditional linear regression task, which is the joint task of identifying a significant segment of the population, described by a k-DNF, along with its linear regression fit.
Cite
@article{arxiv.1806.02326,
title = {Conditional Linear Regression},
author = {Diego Calderon and Brendan Juba and Sirui Li and Zongyi Li and Lisa Ruan},
journal= {arXiv preprint arXiv:1806.02326},
year = {2019}
}