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2020 – today
- 2024
[j5]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. Trans. Mach. Learn. Res. 2024 (2024)- 2023
- [i6]Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. CoRR abs/2301.11447 (2023) - [i5]Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet:
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation. CoRR abs/2306.02071 (2023) - 2022
- [j4]Maxime Vono, Daniel Paulin, Arnaud Doucet:
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. J. Mach. Learn. Res. 23: 25:1-25:69 (2022) - [j3]Maxime Vono
, Nicolas Dobigeon, Pierre Chainais:
High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm. SIAM Rev. 64(1): 3-56 (2022) - [c9]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS 2022: 6459-6500 - [c8]Pierre Palud, Pierre Chainais, Franck Le Petit, Emeric Bron, Pierre-Antoine Thouvenin, Maxime Vono, L. Einig, M. Garcia Santa-Maria, Mathilde Gaudel, Jan H. Orkisz, Victor de Souza Magalhaes, Sébastien Bardeau, Maryvonne Gerin, Javier R. Goicoechea, Pierre Gratier, Viviana V. Guzmán, Jouni Kainulainen, François Levrier, Nicolas Peretto, Jérome Pety, Antoine Roueff, Albrecht Sievers:
Mixture of noises and sampling of non-log-concave posterior distributions. EUSIPCO 2022: 2031-2035 - [c7]Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono:
Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. KDD 2022: 4772-4773 - [c6]Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines:
FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS 2022 - [i4]Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus:
FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR abs/2206.03611 (2022) - 2021
- [j2]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. J. Comput. Graph. Stat. 30(2): 335-348 (2021) - [c5]Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines:
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. ICML 2021: 8577-8587 - [i3]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR abs/2106.00797 (2021) - [i2]Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines:
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR abs/2106.06300 (2021) - 2020
- [b1]Maxime Vono:
Asymptotically exact data augmentation - Models and Monte Carlo sampling with applications to Bayesian inference. (Augmentation de modèles approchée : Modèles et échantillonnage Monte Carlo avec applications à l'inférence Bayésienne). National Polytechnic Institute of Toulouse, France, 2020
2010 – 2019
- 2019
- [j1]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems. IEEE Trans. Signal Process. 67(6): 1648-1661 (2019) - [c4]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Bayesian Image Restoration under Poisson Noise and Log-concave Prior. ICASSP 2019: 1712-1716 - [c3]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models. ICASSP 2019: 5037-5041 - [c2]Maxime Vono, Javier R. Goicoechea, Pierre Gratier, Viviana V. Guzmán, Annie Hughes, Jouni Kainulainen, David Languignon, Jacques Le Bourlot, François Levrier, Harvey S. Listz, Karin I. Oberg, Emeric Bron, Jan H. Orkisz, Nicolas Peretto, Jérome Pety, Antoine Roueff, Èvelyne Roueff, Albrecht Sievers, Victor de Souza Magalhaes, Pascal Tremblin, Pierre Chainais, Franck Le Petit, Sébastien Bardeau, Sébastien Bourguignon, Jocelyn Chanussot, Mathilde Gaudel, Maryvonne Gerin:
A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data. WHISPERS 2019: 1-5 - [i1]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Asymptotically exact data augmentation: models, properties and algorithms. CoRR abs/1902.05754 (2019) - 2018
- [c1]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler. MLSP 2018: 1-6
Coauthor Index
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