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cs.LG2026
Calibeating for general proper losses: A Bregman divergence approach
Maximilian Fichtl, Cristóbal Guzmán, Nishant A. Mehta
This work introduces a general framework for calibeating based on regret minimization. As compared to Foster and Hart's seminal calibeating work which had specialized treatments of…
cs.LG2024
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Michael Menart, Enayat Ullah, Raman Arora +2
We study private empirical risk minimization (ERM) problem for losses satisfying the -Kurdyka-Åojasiewicz (KL) condition. The Polyak-Åojasiewicz (PL) condition is a spec…
cs.LG2024
Differentially Private Generalized Linear Models Revisited
Raman Arora, Raef Bassily, Cristóbal Guzmán +2
We study the problem of -differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case i…