We study the task of testable learning of general -- not necessarily homogeneous -- halfspaces with adversarial label noise with respect to the Gaussian distribution. In the testable learning framework, the goal is to develop a tester-learner such that if the data passes the tester, then one can trust the output of the robust learner on the data.Our main result is the first polynomial time tester-learner for general halfspaces that achieves dimension-independent misclassification error. At the heart of our approach is a new methodology to reduce testable learning of general halfspaces to testable learning of nearly homogeneous halfspaces that may be of broader interest.
@article{arxiv.2408.17165,
title = {Efficient Testable Learning of General Halfspaces with Adversarial Label Noise},
author = {Ilias Diakonikolas and Daniel M. Kane and Sihan Liu and Nikos Zarifis},
journal= {arXiv preprint arXiv:2408.17165},
year = {2024}
}