Reweighted Kernel Machines Learn Sparse Functions Efficiently
Iteratively reweighted kernel machines locate key dimensions after just a few iterations and achieve performance comparable to neural networks in synthetic tests, while staying transparent. getnews.me/reweighted-kernel-machin... #kernelmethods #ml
Scalable Kernel Representation Learning with Nyström Approximation
KREPES uses Nyström approximation for scalable kernel representation learning, matching deep baselines on image benchmarks while keeping interpretability. getnews.me/scalable-kernel-represen... #kernelmethods #nystrom
Kernel Methods for Learning Functions, Operators and Dynamical Systems
Released September 2025, the article outlines kernel‑based learning for scalar functions, operator‑valued kernels and Koopman‑operator system identification for a CIME school. getnews.me/kernel-methods-for-learn... #kernelmethods #koopman
Data-Efficient Kernel Methods Advance Learning of Hamiltonian Systems
Kernel‑based methods identify and forecast Hamiltonian dynamics with fewer data points, preserving energy; tested on mass‑spring oscillator, nonlinear pendulum, and Henon‑Heiles. getnews.me/data-efficient-kernel-me... #kernelmethods #hamiltonian
