106 citations · 297 across the 23 of their papers we have counts for
3 papers · 1 filter
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Markov processes serve as a universal model for many real-world random processes. This paper presents a data-driven approach for learning these models through the spectral decompos…
Neural Conditional Probability for Uncertainty Quantification
Vladimir R. Kostic, Karim Lounici, Gregoire Pacreau +3
We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with a focus on statistical inference tasks. NCP can be used…
Operator World Models for Reinforcement Learning
Pietro Novelli, Marco Pratticò, Massimiliano Pontil +1
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)…