Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. - Vladimir Kostic; Pietro Novelli; Andreas Maurer; Carlo Ciliberto; Lorenzo Rosasco; Massimiliano Pontil (NeurIPS, 2022)

Characterizing metastable states with the help of machine learning. - Pietro Novelli; Luigi Bonati; Massimiliano Pontil; Michele Parrinello (CoRR, 2022)

Learning dynamical systems: an example from open quantum system dynamics. - Pietro Novelli (CoRR, 2022)

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. - John Isak Texas Falk; Luigi Bonati; Pietro Novelli; Michele Parrinello; Massimiliano Pontil (NeurIPS, 2023)

Sharp Spectral Rates for Koopman Operator Learning. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (NeurIPS, 2023)

Estimating Koopman operators with sketching to provably learn large scale dynamical systems. - Giacomo Meanti; Antoine Chatalic; Vladimir Kostic; Pietro Novelli; Massimiliano Pontil; Lorenzo Rosasco (NeurIPS, 2023)

Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (CoRR, 2023)

Deep projection networks for learning time-homogeneous dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (CoRR, 2023)

A randomized algorithm to solve reduced rank operator regression. - Giacomo Turri; Vladimir Kostic; Pietro Novelli; Massimiliano Pontil (CoRR, 2023)

Learning invariant representations of time-homogeneous stochastic dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (ICLR, 2024)

Consistent Long-Term Forecasting of Ergodic Dynamical Systems. - Vladimir R. Kostic; Karim Lounici; Prune Inzerilli; Pietro Novelli; Massimiliano Pontil (ICML, 2024)

Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. - Daniel Felipe Ordoñez Apraez; Vladimir Kostic; Giulio Turrisi; Pietro Novelli; Carlos Mastalli; Claudio Semini; Massimiliano Pontil (L4DC, 2024)

Neural Conditional Probability for Uncertainty Quantification. - Vladimir Kostic; Grégoire Pacreau; Giacomo Turri; Pietro Novelli; Karim Lounici; Massimiliano Pontil (NeurIPS, 2024)

Operator World Models for Reinforcement Learning. - Pietro Novelli; Marco Pratticò; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2024)

Neural Conditional Probability for Inference. - Vladimir R. Kostic; Karim Lounici; Grégoire Pacreau; Pietro Novelli; Giacomo Turri; Massimiliano Pontil (CoRR, 2024)

Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. - Vladimir R. Kostic; Karim Lounici; Hélène Halconruy; Timothee Devergne; Pietro Novelli; Massimiliano Pontil (CoRR, 2024)

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems. - Giacomo Turri; Luigi Bonati; Kai Zhu; Massimiliano Pontil; Pietro Novelli (CoRR, 2025)