Learning Weakly Convex Sets in Metric Spaces

arXiv:2105.06251v2 Announce Type: replace
Abstract: One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a {consistent} hypothesis, i.e., which has zero training error. While problems involving {\em convex} hypotheses have been extensively studied, the question of whether efficient learning is possible for non-convex hypotheses composed of possibly several disconnected regions is still less understood. Although it has been shown quite …

abstract arxiv class consistent cs.ai cs.lg error hypothesis machine machine learning question spaces stat.ml theory training type