ML Anthology

Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression

Ben Lengerich, Bryon Aragam, Eric Xing
ICMLW 2019
/icmlw/2019/lengerich2019icmlw-every/

Abstract

When data arise from multiple latent subpopulations, machine learning frameworks typically estimate parameter values independently for each sub-population. In this paper, we propose to overcome these limits by considering samples as tasks in a multitask learning framework.

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Cite

Text

Lengerich et al. "Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression." ICML 2019 Workshops: AMTL, 2019.

Markdown

[Lengerich et al. "Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression." ICML 2019 Workshops: AMTL, 2019.](https://mlanthology.org/icmlw/2019/lengerich2019icmlw-every/)

BibTeX

@inproceedings{lengerich2019icmlw-every,
  title     = {{Every Sample a Task: Pushing the Limits of Heterogeneous Models with Personalized Regression}},
  author    = {Lengerich, Ben and Aragam, Bryon and Xing, Eric},
  booktitle = {ICML 2019 Workshops: AMTL},
  year      = {2019},
  url       = {https://mlanthology.org/icmlw/2019/lengerich2019icmlw-every/}
}