Proper Agnostic Learning of Functions of Halfspaces under Gaussian Marginals

ArXi:2605.27594v1 Announce Type: cross We study the problem of computationally efficient proper agnostic learning of multidimensional concept classes under the Gaussian distribution. In this setting, given i.i.d. labeled samples from an unknown distribution over $\mathbb{R}^d \times \{\pm 1\}$ whose marginal on $\mathbb{R}^d$ is Gaussian, the goal is to output a hypothesis from a target class $\mathcal{F}$ whose 0-1 loss is within $\epsilon$ of that of the best classifier in $\mathcal{F