An Exploration of Multicalibration Uniform Convergence Bounds

Recent works have investigated the sample complexity necessary for fair machine learning. The most advanced of such sample complexity bounds are developed by analyzing multicalibration uniform convergence for a given predictor class. We present a framework which yields multicalibration error uniform...

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Hauptverfasser:	Rosenberg, Harrison, Bhattacharjee, Robi, Fawaz, Kassem, Jha, Somesh
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning
Online-Zugang:	Volltext bestellen
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