16 citations · 25 across the 7 of their papers we have counts for
7 papers
Scalable Pseudospectral Analysis via Low-Rank Approximations of Dynamical Systems
Vladimir R. Kostic, Dragana Lj. Cvetkovic, Ljiljana Cvetkovic
Pseudospectral analysis is fundamental for quantifying the sensitivity and transient behavior of nonnormal matrices, yet its computational cost scales cubically with dimension, ren…
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…
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
Vladimir Kostic, Pietro Novelli, Andreas Maurer +3
We study a class of dynamical systems modelled as Markov chains that admit an invariant distribution via the corresponding transfer, or Koopman, operator. While data-driven algorit…
Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport
Vladimir Kostic, Saverio Salzo, Massimilano Pontil
In this work we propose a batch version of the Greenkhorn algorithm for multimarginal regularized optimal transport problems. Our framework is general enough to cover, as particula…
The method of Bregman projections in deterministic and stochastic convex feasibility problems
Vladimir Kostic, Saverio Salzo
In this work we study the method of Bregman projections for deterministic and stochastic convex feasibility problems with three types of control sequences for the selection of sets…