Conditional Linear Regression

Authors

  • Diego Calderon University of Arkansas
  • Brendan Juba Washington University in St. Louis
  • Zongyi Li Washington University in St. Louis
  • Lisa Ruan M.I.T.

Abstract

Previous 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. By contrast, we may be interested in finding a segment of the population for which we can find a linear rule capable of achieving more accurate predictions. We give an efficient algorithm 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.

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Published

2018-04-29

How to Cite

Calderon, D., Juba, B., Li, Z., & Ruan, L. (2018). Conditional Linear Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12200