Maxime Vono

According to our database, Maxime Vono authored at least 21 papers between 2018 and 2025.

Collaborative distances:

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

2025

CriteoPrivateAd: A Real-World Bidding Dataset to Design Private Advertising Systems.

Mehdi Sebbar, Corentin Odic, Mathieu Léchine, Aloïs Bissuel, Nicolas Chrysanthos, Anthony D'Amato, Alexandre Gilotte, Fabian Höring, Sarah Nogueira, Maxime Vono

CoRR, February, 2025

On the Impact of the Utility in Semivalue-based Data Valuation.

Mélissa Tamine, Benjamin Heymann, Patrick Loiseau, Maxime Vono

CoRR, February, 2025

Distribution-Aware Mean Estimation under User-level Local Differential Privacy.

Corentin Pla, Maxime Vono, Hugo Richard

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Personalised Federated Learning On Heterogeneous Feature Spaces.

Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola

Trans. Mach. Learn. Res., 2024

Open Research Challenges for Private Advertising Systems Under Local Differential Privacy.

Matilde Tullii, Solenne Gaucher, Hugo Richard, Eustache Diemert, Vianney Perchet, Alain Rakotomamonjy, Clément Calauzènes, Maxime Vono

Proceedings of the Web Information Systems Engineering - WISE 2024, 2024

DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation.

Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2022

High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

SIAM Rev., 2022

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting.

Maxime Vono, Daniel Paulin, Arnaud Doucet

J. Mach. Learn. Res., 2022

FedPop: A Bayesian Approach for Personalised Federated Learning.

Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search.

Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Mixture of noises and sampling of non-log-concave posterior distributions.

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

Proceedings of the 30th European Signal Processing Conference, 2022

QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.

Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines

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

2021

Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

J. Comput. Graph. Stat., 2021

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm.

Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines

CoRR, 2021

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs.

Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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).

Maxime Vono

PhD thesis, 2020

2019

Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

IEEE Trans. Signal Process., 2019

A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data.

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

Proceedings of the 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, 2019

Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

Proceedings of the IEEE International Conference on Acoustics, 2019

Bayesian Image Restoration under Poisson Noise and Log-concave Prior.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler.

Maxime Vono, Nicolas Dobigeon, Pierre Chainais

Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018