4 citations · 11 across the 8 of their papers we have counts for
8 papers
A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
Antoine Chatalic, Marco Letizia, Nicolas Schreuder +1
Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scien…
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé +4
In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems. While kernel methods have typical…
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito +1
In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the in…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
MM: A general method to perform various data analysis tasks from a differentially private sketch
Florimond Houssiau, Vincent Schellekens, Antoine Chatalic +2
Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with…
Nyström Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi +1
Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing…
Mean Nyström Embeddings for Adaptive Compressive Learning
Antoine Chatalic, Luigi Carratino, Ernesto De Vito +1
Compressive learning is an approach to efficient large scale learning based on sketching an entire dataset to a single mean embedding (the sketch), i.e. a vector of generalized mom…
Sketching Datasets for Large-Scale Learning (long version)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven +3
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification…