A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

We introduce a tunable loss function called α-loss, parameterized by α ∈ (0,∞], which interpolates between the exponential loss (α = 1/2), the log-loss (α = 1), and the 0-1 loss (α = ∞), for the machine learning...

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Bibliographic Details
Main Authors:	Cava, J.K (Author), Dasarathy, G. (Author), Diaz, M. (Author), Kairouz, P. (Author), Sankar, L. (Author), Sypherd, T. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	α α-loss Arimoto conditional entropy Calibration Classification (of information) Classification algorithm Classification algorithms classification-calibration Classification-calibration Conditional entropy Entropy Generalisation generalization Logistics -loss Neural networks Noise measurement Noise measurements Optimisations Optimization Privacy Quasi convexity robustness Robustness strictly local quasi-convexity Strictly local quasi-convexity
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