3 citations · 3 across the 1 of their papers we have counts for
6 papers
Nonparametric Control Koopman Operators
Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3
This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictiona…
Operator Models for Continuous-Time Offline Reinforcement Learning
Nicolas Hoischen, Petar Bevanda, Max Beier +3
Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environmen…
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski +2
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where b…
Sequence Modeling with Spectral Mean Flows
Jinwoo Kim, Max Beier, Petar Bevanda +2
A key question in sequence modeling with neural networks is how to represent and learn highly nonlinear and probabilistic state dynamics. Operator theory views such dynamics as lin…
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Petar Bevanda, Nicolas Hoischen, Tobias Wittmann +3
This paper presents a novel operator-theoretic approach for optimal control of nonlinear stochastic systems within reproducing kernel Hilbert spaces. Our learning framework leverag…
Koopman-Equivariant Gaussian Processes
Petar Bevanda, Max Beier, Armin Lederer +3
Credible forecasting and representation learning of dynamical systems are of ever-increasing importance for reliable decision-making. To that end, we propose a family of Gaussian p…