Agnostic learning in (almost) optimal time via Gaussian surface area

ArXi:2603.06027v1 Announce Type: new The complexity of learning a concept class under Gaussian marginals in the difficult agnostic model is closely related to its $L_1$-approximability by low-degree polynomials. For any concept class with Gaussian surface area at most $\Gamma$, Klivans show that degree $d = O(\Gamma^2 / \varepsilon^4)$ suffices to achieve an $\varepsilon$-approximation. This leads to the best-known bounds on the complexity of learning a variety of concept classes.