Efficient Optimal PAC Learning

Mikael Møller Høgsgaard

Abstract:

Recent advances in the binary classification setting by Hanneke (2016) and Larsen (2023) have
resulted in optimal PAC learners. These learners leverage, respectively, a clever deterministic subsam-
pling scheme and the classic heuristic of bagging Breiman (1996). Both optimal PAC learners use, as
a subroutine, the natural algorithm of empirical risk minimization. Consequently, the computational
cost of these optimal PAC learners is tied to that of the empirical risk minimizer algorithm.
In this work, we seek to provide an alternative perspective on the computational cost imposed by the
link to the empirical risk minimizer algorithm. To this end, we show the existence of an optimal PAC
learner, which offers a different tradeoff in terms of the computational cost induced by the empirical
risk minimizer.

Submission Number: 52