3 papers
stat.ML2025
Minimax learning rates for estimating binary classifiers under margin conditions
Jonathan García, Philipp Petersen
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…
cs.LG2025
High-dimensional classification problems with Barron regular boundaries under margin conditions
Jonathan García, Philipp Petersen
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…
cs.LG2024
Dimension-independent learning rates for high-dimensional classification problems
Andres Felipe Lerma-Pineda, Philipp Petersen, Simon Frieder +1
We study the problem of approximating and estimating classification functions that have their decision boundary in the space. Functions of type arise naturally as s…