Maxime Vono - RankMe

27 papers A* 4A 2Misc 2Journal 14Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	CriteoPrivateAd: A Real-World Bidding Dataset to Design Private Advertising Systems. CoRR Mehdi Sebbar, Corentin Odic, Mathieu Léchine, Aloïs Bissuel, Nicolas Chrysanthos, Anthony D'Amato, Alexandre Gilotte, Fabian Höring, Sarah Nogueira, Maxime Vono
2025	A	`conf`	Distribution-Aware Mean Estimation under User-level Local Differential Privacy. AISTATS Corentin Pla, Maxime Vono, Hugo Richard
2025	J	`jnl`	On the Impact of the Utility in Semivalue-based Data Valuation. CoRR Mélissa Tamine, Benjamin Heymann, Patrick Loiseau, Maxime Vono
2024	A*	`conf`	DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation. NeurIPS Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet
2024	J	`jnl`	Distribution-Aware Mean Estimation under User-level Local Differential Privacy. CoRR Corentin Pla, Hugo Richard, Maxime Vono
2024	—	`conf`	Open Research Challenges for Private Advertising Systems Under Local Differential Privacy. WISE (5) Matilde Tullii, Solenne Gaucher, Hugo Richard, Eustache Diemert, Vianney Perchet, Alain Rakotomamonjy, Clément Calauzènes, Maxime Vono
2024	J	`jnl`	Personalised Federated Learning On Heterogeneous Feature Spaces. Trans. Mach. Learn. Res. Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola
2023	J	`jnl`	DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation. CoRR Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet
2023	J	`jnl`	Personalised Federated Learning On Heterogeneous Feature Spaces. CoRR Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola
2022	J	`jnl`	Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. J. Mach. Learn. Res. Maxime Vono, Daniel Paulin, Arnaud Doucet
2022	A*	`conf`	FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines
2022	J	`jnl`	FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus
2022	J	`jnl`	High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm. SIAM Rev. Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2022	—	`conf`	Mixture of noises and sampling of non-log-concave posterior distributions. EUSIPCO 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
2022	A	`conf`	QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
2022	A*	`conf`	Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search. KDD Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono
2021	J	`jnl`	Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. J. Comput. Graph. Stat. Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2021	A*	`conf`	DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. ICML Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
2021	J	`jnl`	DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
2021	J	`jnl`	QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
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
2019	—	`conf`	A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data. WHISPERS 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
2019	J	`jnl`	Asymptotically exact data augmentation: models, properties and algorithms. CoRR Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2019	Misc	`conf`	Bayesian Image Restoration under Poisson Noise and Log-concave Prior. ICASSP Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2019	Misc	`conf`	Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models. ICASSP Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2019	J	`jnl`	Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems. IEEE Trans. Signal Process. Maxime Vono, Nicolas Dobigeon, Pierre Chainais
2018	—	`conf`	Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler. MLSP Maxime Vono, Nicolas Dobigeon, Pierre Chainais