Maxime Vono

Orcid: 0000-0003-4859-965X

According to our database1, Maxime Vono authored at least 17 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation.
CoRR, 2023

Personalised Federated Learning On Heterogeneous Feature Spaces.
CoRR, 2023

2022
High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm.
SIAM Rev., 2022

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting.
J. Mach. Learn. Res., 2022

FedPop: A Bayesian Approach for Personalised Federated Learning.
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.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022


QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms.
J. Comput. Graph. Stat., 2021

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm.
CoRR, 2021

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs.
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).
PhD thesis, 2020

2019
Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems.
IEEE Trans. Signal Process., 2019


Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models.
Proceedings of the IEEE International Conference on Acoustics, 2019

Bayesian Image Restoration under Poisson Noise and Log-concave Prior.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018


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