Efficiently Learning Adversarially Robust Halfspaces with Noise

Jul 12, 2020

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We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. We give the first computationally efficient algorithm for this problem in the realizable setting and in the presence of random label noise with respect to any ℓ_p-perturbation (and, more generally, perturbations with respect to any norm).

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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