Title: Non-Parametric Compressed Learning of Dynamical Systems.

Abstract: The mature field of systems theory has enabled the success of model-based decision-making. Model identification typically requires fitting parametric models to data from interaction with the environments. In this talk, I will discuss an operator-theoretic approach to learn compressed representations of nonlinear dynamics from data with provable guarantees. We first build an analytically tractable representation of system dynamics via the conditional mean embedding operator that interacts with a reproducing kernel Hilbert space (RKHS). Then, we allow selective loss in the representation of that operator to control model complexity. I will discuss sample complexity guarantees to learn such operators in centralized and decentralized settings and present applications to reinforcement learning, power system transient stability analysis, and uncertainty propagation.