• Corpus ID: 220830740

# Distributionally Robust Losses for Latent Covariate Mixtures

@article{Duchi2020DistributionallyRL,
title={Distributionally Robust Losses for Latent Covariate Mixtures},
author={John C. Duchi and Tatsunori B. Hashimoto and Hongseok Namkoong},
journal={ArXiv},
year={2020},
volume={abs/2007.13982}
}
• Published 28 July 2020
• Computer Science
• ArXiv
While modern large-scale datasets often consist of heterogeneous subpopulations---for example, multiple demographic groups or multiple text corpora---the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations. We propose a convex procedure that controls the worst-case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically…

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## References

SHOWING 1-10 OF 70 REFERENCES
Learning Models with Uniform Performance via Distributionally Robust Optimization
• Computer Science, Mathematics
ArXiv
• 2018
A distributionally robust stochastic optimization framework that learns a model providing good performance against perturbations to the data-generating distribution is developed, and a convex formulation for the problem is given, providing several convergence guarantees.
Robust Covariate Shift Prediction with General Losses and Feature Views
• Computer Science
ArXiv
• 2017
By robustly minimizing various loss functions, including non-convex ones, under the testing distribution; and by separately shaping the influence of covariate shift according to different feature-based views of the relationship between input variables and example labels, these generalizations make robust covariateshift prediction applicable to more task scenarios.
Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
• Computer Science
Math. Program.
• 2018
It is demonstrated that the distributionally robust optimization problems over Wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs.
DATA-DRIVEN OPTIMAL TRANSPORT COST SELECTION FOR DISTRIBUTIONALLY ROBUST OPTIMIZATION
• Computer Science
• 2019
This paper shows rigorously that its framework encompasses adaptive regularization as a particular case, and demonstrates empirically that the proposed methodology is able to improve upon a wide range of popular machine learning estimators.
Distributionally Robust Logistic Regression
• Computer Science, Mathematics
NIPS
• 2015
This paper uses the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples, and proposes a distributionally robust logistic regression model that minimizes a worst-case expected logloss function.
Wasserstein Distributional Robustness and Regularization in Statistical Learning
• Computer Science
ArXiv
• 2017
A broad class of loss functions are identified, for which the Wasserstein DRSO is asymptotically equivalent to a regularization problem with a gradient-norm penalty, which suggests a principled way to regularize high-dimensional, non-convex problems.
Robust Wasserstein profile inference and applications to machine learning
• Computer Science
J. Appl. Probab.
• 2019
Wasserstein Profile Inference is introduced, a novel inference methodology which extends the use of methods inspired by Empirical Likelihood to the setting of optimal transport costs (of which Wasserstein distances are a particular case).
Confidence Intervals for Maximin Effects in Inhomogeneous Large-Scale Data
• Mathematics
• 2016
One challenge of large-scale data analysis is that the assumption of an identical distribution for all samples is often not realistic. An optimal linear regression might, for example, be markedly
Fairness Without Demographics in Repeated Loss Minimization
• Computer Science
ICML
• 2018
This paper develops an approach based on distributionally robust optimization (DRO), which minimizes the worst case risk over all distributions close to the empirical distribution and proves that this approach controls the risk of the minority group at each time step, in the spirit of Rawlsian distributive justice.
Robust Classification Under Sample Selection Bias
• Computer Science
NIPS
• 2014
This work develops a framework for learning a robust bias-aware (RBA) probabilistic classifier that adapts to different sample selection biases using a minimax estimation formulation and demonstrates the behavior and effectiveness of the approach on binary classification tasks.