10 papers
Representation Learning for Equivariant Inference with Guarantees
Daniel Ordoñez-Apraez, Vladimir KostiÄ, 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…
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…
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…
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…
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…