Timothée Devergne — accepted AI-conference papers

2025

Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups

Vladimir R Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil

ICML 2025poster

Markov processes serve as universal models for many real-world random processes. This paper presents a data-driven approach to learning these models through the spectral decomposition of the infinitesimal generator (IG) of the Markov semigroup. Its unbounded nature complicates traditional methods s…

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2024

From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach

Timothée Devergne, Vladimir R Kostic, Michele Parrinello, Massimiliano Pontil

NeurIPS 2024poster

We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dynamics. Many physical or chemical processes described by this equation involve transitions between metastable states sep…

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2024

Learning the Infinitesimal Generator of Stochastic Diffusion Processes

Vladimir R Kostic, Hélène Halconruy, Timothée Devergne, Karim Lounici, Massimiliano Pontil

NeurIPS 2024poster

We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems. The unbounded nature of the generator poses significant challenges, rendering conventional analysis techniques for Hilb…

Cited by 4 Source ↗PDF ↗Scholar ↗