Formalizes improvement-aware strategic classification for linear classifiers under single-index models, proves the strategic-optimal classifier is a parallel shift of the Bayes boundary, and supplies PAC guarantees with a plug-in algorithm evaluated on datasets.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.
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Linear Strategic Classification with Endogenous Improvements
Formalizes improvement-aware strategic classification for linear classifiers under single-index models, proves the strategic-optimal classifier is a parallel shift of the Bayes boundary, and supplies PAC guarantees with a plug-in algorithm evaluated on datasets.
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Exploring the impact of fairness-aware criteria in AutoML
Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.