Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Novelli, Pietro"'
Autor:
Kostic, Vladimir R., Lounici, Karim, Pacreau, Gregoire, Novelli, Pietro, Turri, Giacomo, Pontil, Massimiliano
We introduce NCP (Neural Conditional Probability), a novel operator-theoretic approach for learning conditional distributions with a particular focus on inference tasks. NCP can be used to build conditional confidence regions and extract important st
Externí odkaz:
http://arxiv.org/abs/2407.01171
Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We addr
Externí odkaz:
http://arxiv.org/abs/2406.19861
We present and analyze an algorithm designed for addressing vector-valued regression problems involving possibly infinite-dimensional input and output spaces. The algorithm is a randomized adaptation of reduced rank regression, a technique to optimal
Externí odkaz:
http://arxiv.org/abs/2312.17348
We study the evolution of distributions under the action of an ergodic dynamical system, which may be stochastic in nature. By employing tools from Koopman and transfer operator theory one can evolve any initial distribution of the state forward in t
Externí odkaz:
http://arxiv.org/abs/2312.13426
Autor:
Ordoñez-Apraez, Daniel, Kostic, Vladimir, Turrisi, Giulio, Novelli, Pietro, Mastalli, Carlos, Semini, Claudio, Pontil, Massimiliano
We introduce the use of harmonic analysis to decompose the state space of symmetric robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces that capture distinct, symmetric, and synergistic motions. For linear dynamics,
Externí odkaz:
http://arxiv.org/abs/2312.07457
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 faithfully captures its dynamics. This is instrumental to learning the
Externí odkaz:
http://arxiv.org/abs/2307.09912
Autor:
Meanti, Giacomo, Chatalic, Antoine, Kostic, Vladimir R., Novelli, Pietro, Pontil, Massimiliano, Rosasco, Lorenzo
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 regression (PCR) or reduced rank regression (RRR) in kernel spaces ca
Externí odkaz:
http://arxiv.org/abs/2306.04520
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally demanding. To
Externí odkaz:
http://arxiv.org/abs/2306.01589
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 operator and its spectral decomposition from data is enabled by a numbe
Externí odkaz:
http://arxiv.org/abs/2302.02004
Autor:
Novelli, Pietro
Machine learning algorithms designed to learn dynamical systems from data can be used to forecast, control and interpret the observed dynamics. In this work we exemplify the use of one of such algorithms, namely Koopman operator learning, in the cont
Externí odkaz:
http://arxiv.org/abs/2211.06678