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arXiv · 2301.06195

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees

Songkai Xue·Yuekai Sun·Mikhail Yurochkin
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Abstract

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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Songkai Xue, Yuekai Sun, Mikhail Yurochkin. 2023-01-15. Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees. https://arxiv.org/abs/2301.06195

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