Minimax learning rates for estimating binary classifiers under margin conditions

We study classification problems using binary estimators where the decision boundary is described by horizon functions and where the data distribution satisfies a geometric margin condition. We establish upper and lower bounds for the minimax learning rate over broad function classes with bounded Kolmogorov entropy in Lebesgue norms. A key novelty of our work is the derivation of lower bounds on the worst-case learning rates under a geometric margin condition -- a setting that is almost universally satisfied in practice but remains theoretically challenging. Moreover, our results deal with the noiseless setting, where lower bounds are particularly hard to establish. We apply our general results to classification problems with decision boundaries belonging to several function classes: for Barron-regular functions, and for H\"older-continuous functions with strong margins, we identify optimal rates close to the fast learning rates of $\mathcal{O}(n^{-1})$ for $n \in \mathbb{N}$ samples. Also for merely convex decision boundaries, in a strong margin case optimal rates near $\mathcal{O}(n^{-1/2})$ can be achieved.

High-dimensional classification problems with Barron regular boundaries under margin conditions

We prove that a classifier with a Barron-regular decision boundary can be approximated with a rate of high polynomial degree by ReLU neural networks with three hidden layers when a margin condition is assumed. In particular, for strong margin conditions, high-dimensional discontinuous classifiers can be approximated with a rate that is typically only achievable when approximating a low-dimensional smooth function. We demonstrate how these expression rate bounds imply fast-rate learning bounds that are close to $n^{-1}$ where $n$ is the number of samples. In addition, we carry out comprehensive numerical experimentation on binary classification problems with various margins. We study three different dimensions, with the highest dimensional problem corresponding to images from the MNIST data set.