mlsanityData to 7 Oct 2026

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees

arXiv 2301.06195 ↗First posted 15 Jan 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

We consider the task of training machine learning models with data-dependent constraints. Such constraints often arise as empirical versions of expected value constraints that enforce fairness or stability goals. We reformulate data-dependent constraints so that they are calibrated: enforcing the reformulated constraints guarantees that their expected value counterparts are satisfied with a user-prescribed probability. The resulting optimization problem is amendable to standard stochastic optimization algorithms, and we demonstrate the efficacy of our method on a fairness-sensitive classification task where we wish to guarantee the classifier's fairness (at test time).

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References · 17

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2017Counterfactual Fairnesscited by 802
  3. 2018A Reductions Approach to Fair Classificationcited by 585
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  5. 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 285
  6. 2016Statistics of Robust Optimization: A Generalized Empirical Likelihood Approachcited by 182
  7. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
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  9. 2015Distributionally Robust Logistic Regressioncited by 124
  10. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  11. 2016Satisfying Real-world Goals with Dataset Constraintscited by 68
  12. 2018Two-Player Games for Efficient Non-Convex Constrained Optimizationcited by 55
5 more
  1. 2018Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraintscited by 53
  2. 2016Scalable Learning of Non-Decomposable Objectivescited by 40
  3. 2020SenSeI: Sensitive Set Invariance for Enforcing Individual Fairnesscited by 37
  4. 2016Recovering Best Statistical Guarantees via the Empirical Divergence-based Distributionally Robust Optimizationcited by 33
  5. 2017Robust Regression via Mutivariate Regression Depthcited by 20

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