Inference and optimization algorithms usually have hyperparameters that require to be
tuned in order to achieve efficiency. We consider here different approaches to efficiently …

Long version of the paper. This long version of the paper features an additional evaluated
method for Section 2.2 (AM with posterior reordering), examples of the behavior of AMOR on …

When targeting a distribution that is artificially invariant under some permutations, Markov
chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal …

In machine learning (ML), the training process of an algorithm generally consists of two
nested loops: the outer loop iterates over hyperparameter values, while the inner loop …

Peut-on identifier un appareil électrique à partir de l'énergie électrique qu'il consomme?
C'est essentiellement cette question qui guide les travaux de recherche présentés dans …

When targeting a distribution that is artificially invariant under some permutations, Markov
chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering marginal …