208 citations · 1.5k across the 43 of their papers we have counts for
47 papers
Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach
Sebastian Reboul, Hélène Halconruy
We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.…
On the Asymptotics of Importance Weighted Variational Inference
Badr-Eddine Cherief-Abdellatif, Randal Douc, Arnaud Doucet +1
For complex latent variable models, the likelihood function is not available in closed form. In this context, a popular method to perform parameter estimation is Importance Weighte…
Variational Diffusion Posterior Sampling with Midpoint Guidance
Badr Moufad, Yazid Janati, Lisa Bedin +4
Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distr…
Self-Organizing State-Space Models with Artificial Dynamics
Yuan Chen, Mathieu Gerber, Christophe Andrieu +1
We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter . A popular idea to address this problem cons…
Asymptotic convergence of iterative optimization algorithms
Randal Douc, Sylvain Le Corff
This paper introduces a general framework for iterative optimization algorithms and establishes under general assumptions that their convergence is asymptotically geometric. We als…
The Importance Markov Chain
Charly Andral, Randal Douc, Hugo Marival +1
The Importance Markov chain is a novel algorithm bridging the gap between rejection sampling and importance sampling, moving from one to the other through a tuning parameter. Based…