Conditional Linear Regression

Diego Calderon, Brendan Juba, Zongyi Li, Lisa Ruan

AAAI 2018 pp. 8055-8056

doi:10.1609/AAAI.V32I1.12200 /aaai/2018/calderon2018aaai-conditional/

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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Text

Calderon et al. "Conditional Linear Regression." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12200

Markdown

[Calderon et al. "Conditional Linear Regression." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/calderon2018aaai-conditional/) doi:10.1609/AAAI.V32I1.12200

BibTeX

@inproceedings{calderon2018aaai-conditional,
  title     = {{Conditional Linear Regression}},
  author    = {Calderon, Diego and Juba, Brendan and Li, Zongyi and Ruan, Lisa},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8055-8056},
  doi       = {10.1609/AAAI.V32I1.12200},
  url       = {https://mlanthology.org/aaai/2018/calderon2018aaai-conditional/}
}