Antoine Chatalic

According to our database, Antoine Chatalic authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method.

Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé, Cesare Molinari, Carlos Ocampo-Martinez, Carme Torras, Lorenzo Rosasco

CoRR, 2024

2023

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling.

Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco

CoRR, 2023

Estimating Koopman operators with sketching to provably learn large scale dynamical systems.

Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees.

Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé, Lorenzo Rosasco, Carme Torras

Proceedings of the Conference on Robot Learning, 2023

2022

M²M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch.

Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye

Proceedings of the Security and Trust Management - 18th International Workshop, 2022

Nyström Kernel Mean Embeddings.

Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi

Proceedings of the International Conference on Machine Learning, 2022

Mean Nyström Embeddings for Adaptive Compressive Learning.

Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Sketching Data Sets for Large-Scale Learning: Keeping only what you need.

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

IEEE Signal Process. Mag., 2021

2020

Efficient and Privacy-Preserving Compressive Learning. (Méthodes efficaces pour l'apprentissage compressif avec garanties de confidentialité).

Antoine Chatalic

PhD thesis, 2020

Sketching Datasets for Large-Scale Learning (long version).

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

CoRR, 2020

2019

Sketched Clustering via Hybrid Approximate Message Passing.

Evan Byrne, Antoine Chatalic, Rémi Gribonval, Philip Schniter

IEEE Trans. Signal Process., 2019

Differentially Private Compressive K-means.

Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Large-Scale High-Dimensional Clustering with Fast Sketching.

Antoine Chatalic, Rémi Gribonval, Nicolas Keriven

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018