May 16, 2024, 4:41 a.m. | Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.08892v1 Announce Type: new
Abstract: Randomized smoothing has shown promising certified robustness against adversaries in classification tasks. Despite such success with only zeroth-order access to base models, randomized smoothing has not been extended to a general form of regression. By defining robustness in regression tasks flexibly through probabilities, we demonstrate how to establish upper bounds on input data point perturbation (using the $\ell_2$ norm) for a user-specified probability of observing valid outputs. Furthermore, we showcase the asymptotic property of a …

arxiv cs.lg regression robust through type

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