27 citations · 47 across the 10 of their papers we have counts for
3 papers · 1 filter
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (P…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito +1
We study the behavior of error bounds for multiclass classification under suitable margin conditions. For a wide variety of methods we prove that the classification error under a h…