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Provable tradeoffs in adversarially robust classification
[article]
2022
arXiv
pre-print
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open problems remain. In this paper, we address several key questions. We derive exact and approximate Bayes-optimal robust classifiers for the important setting of two- and three-class Gaussian classification problems with arbitrary imbalance, for ℓ_2 and ℓ_∞ adversaries. In contrast to classical Bayes-optimal
arXiv:2006.05161v5
fatcat:ic3unrv27vaerfofok4d4jnpbi