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Found 2 papers, 1 papers with code
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
In particular, we provide a sharp asymptotic characterisation of M-estimators trained on a family of elliptical covariate and noise data distributions including cases where second and higher moments do not exist.
Unveiling the Hessian's Connection to the Decision Boundary
Based on this finding, we provide a new and straightforward approach to studying the complexity of a high-dimensional decision boundary; show that this connection naturally inspires a new generalization measure; and finally, we develop a novel margin estimation technique which, in combination with the generalization measure, precisely identifies minima with simple wide-margin boundaries.
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