3 papers
cs.LG2026
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
Jean-Marc Mercier, Gabriele Santin
Deep neural networks dominate modern machine learning, while alternative function approximators remain comparatively underexplored at scale. In this work, we revisit kernel methods…
math.NA2025
Reproducing kernel methods for machine learning, PDEs, and statistics
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
This monograph develops a unified, application-driven framework for kernel methods grounded in reproducing kernel Hilbert spaces (RKHS) and optimal transport (OT). Part I lays the…
math.NA2024
A class of kernel-based scalable algorithms for data science
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which, most importantly, are scale up efficiently with la…