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. https://getnews.me/reweighted-kernel-machines-learn-sparse-functions-efficiently/ #kernelmethods #ml

KREPES uses Nyström approximation for scalable kernel representation learning, matching deep baselines on image benchmarks while keeping interpretability. https://getnews.me/scalable-kernel-representation-learning-with-nystrom-approximation/ #kernelmethods #nystrom

Released September 2025, the article outlines kernel‑based learning for scalar functions, operator‑valued kernels and Koopman‑operator system identification for a CIME school. https://getnews.me/kernel-methods-for-learning-functions-operators-and-dynamical-systems/ #kernelmethods #koopman

Kernel‑based methods identify and forecast Hamiltonian dynamics with fewer data points, preserving energy; tested on mass‑spring oscillator, nonlinear pendulum, and Henon‑Heiles. https://getnews.me/data-efficient-kernel-methods-advance-learning-of-hamiltonian-systems/ #kernelmethods #hamiltonian

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