106 citations · 297 across the 23 of their papers we have counts for
7 papers · 1 filter
A randomized algorithm to solve reduced rank operator regression
Giacomo Turri, Vladimir Kostic, Pietro Novelli +1
We present and analyze an algorithm designed for addressing vector-valued regression problems involving possibly infinite-dimensional input and output spaces. The algorithm is a ra…
Consistent Long-Term Forecasting of Ergodic Dynamical Systems
Prune Inzerilli, Vladimir Kostic, Karim Lounici +2
We study the evolution of distributions under the action of an ergodic dynamical system, which may be stochastic in nature. By employing tools from Koopman and transfer operator th…
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling
Daniel Ordoñez-Apraez, Vladimir Kostic, Giulio Turrisi +4
We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capt…
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…
Learning invariant representations of time-homogeneous stochastic dynamical systems
Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi +2
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that f…
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
John Falk, Luigi Bonati, Pietro Novelli +2
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, whi…
Sharp Spectral Rates for Koopman Operator Learning
Vladimir Kostic, Karim Lounici, Pietro Novelli +1
Nonlinear dynamical systems can be handily described by the associated Koopman operator, whose action evolves every observable of the system forward in time. Learning the Koopman o…