Calibrated Data-Dependent Constraints with Exact Satisfaction GuaranteesDownload PDF

Published: 31 Oct 2022, 18:00, Last Modified: 12 Oct 2022, 19:57NeurIPS 2022 AcceptReaders: Everyone
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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