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ORCIDPietro Novelli
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2020 – today
- 2024
[c7]Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil:
Learning invariant representations of time-homogeneous stochastic dynamical systems. ICLR 2024- [c6]Vladimir R. Kostic, Karim Lounici, Prune Inzerilli, Pietro Novelli, Massimiliano Pontil:
Consistent Long-Term Forecasting of Ergodic Dynamical Systems. ICML 2024 - [c5]Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil:
Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. L4DC 2024: 1318-1329 - [i13]Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto:
Operator World Models for Reinforcement Learning. CoRR abs/2406.19861 (2024) - [i12]Vladimir R. Kostic, Karim Lounici, Grégoire Pacreau, Pietro Novelli, Giacomo Turri, Massimiliano Pontil:
Neural Conditional Probability for Inference. CoRR abs/2407.01171 (2024) - [i11]Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothee Devergne, Pietro Novelli, Massimiliano Pontil:
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. CoRR abs/2410.14477 (2024) - 2023
- [c4]John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil:
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. NeurIPS 2023 - [c3]Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Sharp Spectral Rates for Koopman Operator Learning. NeurIPS 2023 - [c2]Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco:
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. NeurIPS 2023 - [i10]Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. CoRR abs/2302.02004 (2023) - [i9]John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil:
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. CoRR abs/2306.01589 (2023) - [i8]Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco:
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. CoRR abs/2306.04520 (2023) - [i7]Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil:
Deep projection networks for learning time-homogeneous dynamical systems. CoRR abs/2307.09912 (2023) - [i6]Daniel Felipe Ordoñez Apraez
, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil:
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling. CoRR abs/2312.07457 (2023) - [i5]Prune Inzerilli, Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Consistent Long-Term Forecasting of Ergodic Dynamical Systems. CoRR abs/2312.13426 (2023) - [i4]Giacomo Turri, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil:
A randomized algorithm to solve reduced rank operator regression. CoRR abs/2312.17348 (2023) - 2022
- [c1]Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. NeurIPS 2022 - [i3]Pietro Novelli, Luigi Bonati, Massimiliano Pontil, Michele Parrinello:
Characterizing metastable states with the help of machine learning. CoRR abs/2204.07391 (2022) - [i2]Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil:
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. CoRR abs/2205.14027 (2022) - [i1]Pietro Novelli:
Learning dynamical systems: an example from open quantum system dynamics. CoRR abs/2211.06678 (2022)
Coauthor Index
aka: Vladimir R. Kostic
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