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2 claims from 1 paper

1. Computer Science › Stochastic Gradient Optimization Techniques

   On the interplay between data structure and loss function in classification problems

   d’Ascoli, Gabrié, Sagun and Biroli · arXiv (Cornell University) · 2021

   Unchecked2 claims
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   1. Unchecked“Using methods from statistical physics, we derive a precise asymptotic expression for the train and test error achieved by random feature models trained to classify such data, which is valid for any convex loss function.”
   2. Unchecked“We study in detail how the data structure affects the double descent curve, and show that in the over-parametrized regime, its impact is greater for logistic loss than for mean-squared loss: the easier the task, the wider the gap in performance at the advant…