Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (MLSP, 2018)

Bayesian Image Restoration under Poisson Noise and Log-concave Prior. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (ICASSP, 2019)

Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (ICASSP, 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; et al. (WHISPERS, 2019)

Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (IEEE Trans. Signal Process., 2019)

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 (2020)

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 (ICML, 2021)

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. - Vincent Plassier; Maxime Vono; Alain Durmus; Eric Moulines (CoRR, 2021)

Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. - Maxime Vono; Nicolas Dobigeon; Pierre Chainais (J. Comput. Graph. Stat., 2021)

QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. - Maxime Vono; Vincent Plassier; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (AISTATS, 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; et al. (EUSIPCO, 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 (KDD, 2022)

FedPop: A Bayesian Approach for Personalised Federated Learning. - Nikita Kotelevskii; Maxime Vono; Alain Durmus; Eric Moulines (NeurIPS, 2022)

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. - Maxime Vono; Daniel Paulin; Arnaud Doucet (J. Mach. Learn. Res., 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)

DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation. - Felipe Garrido-Lucero; Benjamin Heymann; Maxime Vono; Patrick Loiseau; Vianney Perchet (NeurIPS, 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 (WISE (5), 2024)

Personalised Federated Learning On Heterogeneous Feature Spaces. - Alain Rakotomamonjy; Maxime Vono; Hamlet Jesse Medina Ruiz; Liva Ralaivola (Trans. Mach. Learn. Res., 2024)

Distribution-Aware Mean Estimation under User-level Local Differential Privacy. - Corentin Pla; Maxime Vono; Hugo Richard (AISTATS, 2025)

On the Impact of the Utility in Semivalue-based Data Valuation. - Mélissa Tamine; Benjamin Heymann; Patrick Loiseau; Maxime Vono (CoRR, 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, 2025)