no code implementations • 3 Jun 2023 • Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet
The Shapley value has recently been proposed as a principled tool to achieve this goal due to formal axiomatic justification.
no code implementations • 26 Jan 2023 • Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i. e. all clients store their data according to the same schema.
no code implementations • 7 Jun 2022 • Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus
We provide non-asymptotic convergence guarantees for the proposed algorithms and illustrate their performances on various personalised federated learning tasks.
no code implementations • 11 Jun 2021 • Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning.
no code implementations • 1 Jun 2021 • Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients.
1 code implementation • 4 Oct 2020 • Maxime Vono, Nicolas Dobigeon, Pierre Chainais
In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization.
Computation
no code implementations • 31 Aug 2020 • Pierre Gratier, Jérôme Pety, Emeric Bron, Antoine Roueff, Jan H. Orkisz, Maryvonne Gerin, Victor de Souza Magalhaes, Mathilde Gaudel, Maxime Vono, Sébastien Bardeau, Jocelyn Chanussot, Pierre Chainais, Javier R. Goicoechea, Viviana V. Guzmán, Annie Hughes, Jouni Kainulainen, David Languignon, Jacques Le Bourlot, Franck Le Petit, François Levrier, Harvey Liszt, Nicolas Peretto, Evelyne Roueff, Albrecht Sievers
We aim to use multi-molecule line emission to infer NH2 from radio observations.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
no code implementations • 23 May 2019 • Maxime Vono, Daniel Paulin, Arnaud Doucet
Performing exact Bayesian inference for complex models is computationally intractable.
no code implementations • 15 Feb 2019 • Maxime Vono, Nicolas Dobigeon, Pierre Chainais
In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms.