58 citations · 232 across the 13 of their papers we have counts for
13 papers
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono +2
We consider the dataset valuation problem, that is, the problem of quantifying the incremental gain, to some relevant pre-defined utility of a machine learning task, of aggregating…
Personalised Federated Learning On Heterogeneous Feature Spaces
Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz +1
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 sc…
Gas kinematics around filamentary structures in the Orion B cloud
Mathilde Gaudel, Jan H. Orkisz, Maryvonne Gerin +27
Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young hi…
FedPop: A Bayesian Approach for Personalised Federated Learning
Nikita Kotelevskii, Maxime Vono, Eric Moulines +1
Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction,…
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus +1
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Marko…
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Maxime Vono, Vincent Plassier, Alain Durmus +2
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients. FL raises many constraints wh…
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stake issue. Vanilla Cholesky samplers imply a computational cost and memory requirements which…
Quantitative inference of the column densities from 3 mm molecular emission: A case study towards Orion B
Pierre Gratier, Jérôme Pety, Emeric Bron +21
Molecular hydrogen being unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-IR or…
Tracers of the ionization fraction in dense and translucent gas: I. Automated exploitation of massive astrochemical model grids
Emeric Bron, Evelyne Roueff, Maryvonne Gerin +21
The ionization fraction plays a key role in the physics and chemistry of the neutral interstellar medium, from controlling the coupling of the gas to the magnetic field to allowing…
C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: An analysis of the precision achievable when modeling spectral line within the Local Thermodynamic Equilibrium approximation
Antoine Roueff, Maryvonne Gerin, Pierre Gratier +23
CO isotopologue transitions are routinely observed in molecular clouds to probe the column density of the gas, the elemental ratios of carbon and oxygen, and to trace the kinematic…
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet
Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posteri…
Asymptotically exact data augmentation: models, properties and algorithms
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous technique to improve convergence properties, simplify the implementation or reduce the compu…
Split-and-augmented Gibbs sampler - Application to large-scale inference problems
Maxime Vono, Nicolas Dobigeon, Pierre Chainais
This paper derives two new optimization-driven Monte Carlo algorithms inspired from variable splitting and data augmentation. In particular, the formulation of one of the proposed…