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Agnostic Learnability of Halfspaces via Logistic Loss

Ziwei Ji · Kwangjun Ahn · Pranjal Awasthi · Satyen Kale · Stefani Karp

Hall E #1318

Keywords: [ T: Optimization ] [ MISC: Supervised Learning ] [ OPT: Convex ] [ OPT: Learning for Optimization ] [ T: Learning Theory ]


We investigate approximation guarantees provided by logistic regression for the fundamental problem of agnostic learning of homogeneous halfspaces. Previously, for a certain broad class of “well-behaved” distributions on the examples, Diakonikolas et al. (2020) proved an tilde{Omega}(OPT) lower bound, while Frei et al. (2021) proved an tilde{O}(sqrt{OPT}) upper bound, where OPT denotes the best zero-one/misclassification risk of a homogeneous halfspace. In this paper, we close this gap by constructing a well-behaved distribution such that the global minimizer of the logistic risk over this distribution only achieves Omega(sqrt{OPT}) misclassification risk, matching the upper bound in (Frei et al., 2021). On the other hand, we also show that if we impose a radial-Lipschitzness condition in addition to well-behaved-ness on the distribution, logistic regression on a ball of bounded radius reaches tilde{O}(OPT) misclassification risk. Our techniques also show for any well-behaved distribution, regardless of radial Lipschitzness, we can overcome the Omega(sqrt{OPT}) lower bound for logistic loss simply at the cost of one additional convex optimization step involving the hinge loss and attain tilde{O}(OPT) misclassification risk. This two-step convex optimization algorithm is simpler than previous methods obtaining this guarantee, all of which require solving O(log(1/OPT)) minimization problems.

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