488 citations · 3k across the 119 of their papers we have counts for
30 papers · 1 filter
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +1
Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference…
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference
Arnaud Descours, Tom Huix, Arnaud Guillin +3
We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with…
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii +2
Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL…
Fast Rates for Maximum Entropy Exploration
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +7
We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maxim…
On Sampling with Approximate Transport Maps
Louis Grenioux, Alain Durmus, Éric Moulines +1
Transport maps can ease the sampling of distributions with non-trivial geometries by transforming them into distributions that are easier to handle. The potential of this approach…
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks
Louis Leconte, Sholom Schechtman, Eric Moulines
In this paper, we develop a new algorithm, Annealed Skewed SGD - AskewSGD - for training deep neural networks (DNNs) with quantized weights. First, we formulate the training of qua…