16 citations · 25 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
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…