1 citations · 2 across the 5 of their papers we have counts for
7 papers
Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3
Many scientific and machine learning systems, from molecular dynamics to diffusion models and beyond, are governed by stochastic dynamics with low-dimensional structure, evolving o…
Tessellation Localized Transfer learning for nonparametric regression
Hélène Halconruy, Benjamin Bobbia, Paul Lejamtel
Transfer learning aims to improve performance on a target task by leveraging information from related source tasks. We propose a nonparametric regression transfer learning framewor…
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…
Learning the Infinitesimal Generator of Stochastic Diffusion Processes
Vladimir R. Kostic, Karim Lounici, Helene Halconruy +2
We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.…
Evolving privacy: drift parameter estimation for discretely observed i.i.d. diffusion processes under LDP
Chiara Amorino, Arnaud Gloter, Hélène Halconruy
The problem of estimating a parameter in the drift coefficient is addressed for discretely observed independent and identically distributed stochastic differential equations (S…
Malliavin calculus for marked binomial processes: portfolio optimisation in the trinomial model and compound Poisson approximation
Hélène Halconruy
In this paper we develop a stochastic analysis for marked binomial processes, that can be viewed as the discrete analogues of marked Poisson processes. The starting point is the st…
Malliavin and dirichlet structures for independent random variables
Laurent Decreusefond, Hélène Halconruy
On any denumerable product of probability spaces, we construct a Malliavin gradient and then a divergence and a number operator. This yields a Dirichlet structure which can be show…