# Distributionally Robust Losses for Latent Covariate Mixtures

@article{Duchi2022DistributionallyRL,
title={Distributionally Robust Losses for Latent Covariate Mixtures},
author={John C. Duchi and Tatsunori B. Hashimoto and Hongseok Namkoong},
journal={ArXiv},
year={2022},
volume={abs/2007.13982}
}
• Published 28 July 2020
• Computer Science
• ArXiv
Reliable Machine Learning via Structured Distributionally Robust Optimization Data sets used to train machine learning (ML) models often suffer from sampling biases and underrepresent marginalized groups. Standard machine learning models are trained to optimize average performance and perform poorly on tail subpopulations. In “Distributionally Robust Losses for Latent Covariate Mixtures,” John Duchi, Tatsunori Hashimoto, and Hongseok Namkoong formulate a DRO approach for training ML models to…

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