Nathan Doumèche

Fast kernel methods: Sobolev, physics-informed, and additive models

Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size $n$ limits their use on large-scale datasets. In this work, we introduce a scalable framework for kernel regression with complexity $O(n \log n)$, fully leveraging GPU acceleration. The approach …