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Antoine Chatalic
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2020 – today
- 2024
- [i7]Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé, Cesare Molinari, Carlos Ocampo-Martinez, Carme Torras, Lorenzo Rosasco:
Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method. CoRR abs/2403.02811 (2024) - 2023
- [c7]Edoardo Caldarelli
, Antoine Chatalic, Adrià Colomé, Lorenzo Rosasco, Carme Torras:
Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees. CoRL 2023: 3010-3029 - [c6]Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco:
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. NeurIPS 2023 - [i6]Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco:
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. CoRR abs/2306.04520 (2023) - [i5]Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito, Lorenzo Rosasco:
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. CoRR abs/2311.13548 (2023) - 2022
- [c5]Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Mean Nyström Embeddings for Adaptive Compressive Learning. AISTATS 2022: 9869-9889 - [c4]Antoine Chatalic, Nicolas Schreuder, Lorenzo Rosasco, Alessandro Rudi:
Nyström Kernel Mean Embeddings. ICML 2022: 3006-3024 - [c3]Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye:
M2M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch. STM 2022: 117-135 - [i4]Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi, Lorenzo Rosasco:
Nyström Kernel Mean Embeddings. CoRR abs/2201.13055 (2022) - [i3]Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye:
M2M: A general method to perform various data analysis tasks from a differentially private sketch. CoRR abs/2211.14062 (2022) - 2021
- [j2]Rémi Gribonval
, Antoine Chatalic
, Nicolas Keriven, Vincent Schellekens
, Laurent Jacques
, Philip Schniter
:
Sketching Data Sets for Large-Scale Learning: Keeping only what you need. IEEE Signal Process. Mag. 38(5): 12-36 (2021) - [i2]Antoine Chatalic, Luigi Carratino, Ernesto De Vito, Lorenzo Rosasco:
Mean Nyström Embeddings for Adaptive Compressive Learning. CoRR abs/2110.10996 (2021) - 2020
- [b1]Antoine Chatalic:
Efficient and Privacy-Preserving Compressive Learning. (Méthodes efficaces pour l'apprentissage compressif avec garanties de confidentialité). University of Rennes 1, France, 2020 - [i1]Rémi Gribonval, Antoine Chatalic, Nicolas Keriven
, Vincent Schellekens, Laurent Jacques, Philip Schniter:
Sketching Datasets for Large-Scale Learning (long version). CoRR abs/2008.01839 (2020)
2010 – 2019
- 2019
- [j1]Evan Byrne, Antoine Chatalic
, Rémi Gribonval
, Philip Schniter
:
Sketched Clustering via Hybrid Approximate Message Passing. IEEE Trans. Signal Process. 67(17): 4556-4569 (2019) - [c2]Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval:
Differentially Private Compressive K-means. ICASSP 2019: 7933-7937 - 2018
- [c1]Antoine Chatalic, Rémi Gribonval, Nicolas Keriven
:
Large-Scale High-Dimensional Clustering with Fast Sketching. ICASSP 2018: 4714-4718
Coauthor Index
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