7 citations · 9 across the 14 of their papers we have counts for
15 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…
Toeplitz Based Spectral Methods for Data-driven Dynamical Systems
Vladimir R. Kostic, Karim Lounici, Massimiliano Pontil
We introduce a Toeplitz-based framework for data-driven spectral estimation of linear evolution operators in dynamical systems. Focusing on transfer and Koopman operators from equi…
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
Giacomo Turri, Grégoire Pacreau, Giacomo Meanti +8
kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model b…
Toward Scalable and Valid Conditional Independence Testing with Spectral Representations
Alek Fröhlich, Vladimir R. Kostic, Karim Lounici +3
Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions. Exist…
The seeds of the future are in the present: A blind exploration of metastable states
Timothée Devergne, Vladimir Kostic, Massimiliano Pontil +1
In this work, we present a novel type of molecular dynamics simulation that aims at discovering, in a blind way, new metastable states. Using only data coming from an initial unbia…
Representation Learning for Equivariant Inference with Guarantees
Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3
In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatica…
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo +8
Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developi…
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…
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
Timothée Devergne, Vladimir Kostic, Michele Parrinello +1
We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dy…
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.…
Morphological Symmetries in Robotics
Daniel Ordoñez-Apraez, Giulio Turrisi, Vladimir Kostic +6
We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot's morphology, frequently o…
A randomized algorithm to solve reduced rank operator regression
Giacomo Turri, Vladimir Kostic, Pietro Novelli +1
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 ra…
Consistent Long-Term Forecasting of Ergodic Dynamical Systems
Prune Inzerilli, Vladimir Kostic, Karim Lounici +2
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 th…
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling
Daniel Ordoñez-Apraez, Vladimir Kostic, Giulio Turrisi +4
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 capt…