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8 papers · 1 filter
Mixture weights optimisation for Alpha-Divergence Variational Inference
Kamélia Daudel, Randal Douc
This paper focuses on -divergence minimisation methods for Variational Inference. More precisely, we are interested in algorithms optimising the mixture weights of any given mix…
Deconvolution with unknown noise distribution is possible for multivariate signals
Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy
This paper considers the deconvolution problem in the case where the target signal is multidimensional and no information is known about the noise distribution. More precisely, no…
Infinite-dimensional gradient-based descent for alpha-divergence minimisation
Kamélia Daudel, Randal Douc, François Portier
This paper introduces the -descent, an iterative algorithm which operates on measures and performs -divergence minimisation in a Bayesian framework. This gradient-based…
Learning the distribution of latent variables in paired comparison models with round-robin scheduling
Roland Diel, Sylvain Le Corff, Matthieu Lerasle
Paired comparison data considered in this paper originate from the comparison of a large number N of individuals in couples. The dataset is a collection of results of contests betw…
A Multiresolution Analysis Framework for the Statistical Analysis of Incomplete Rankings
Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz
Though the statistical analysis of ranking data has been a subject of interest over the past centuries, especially in economics, psychology or social choice theory, it has been rev…
Multiresolution Analysis of Incomplete Rankings
Stéphan Clémençon, Jérémie Jakubowicz, Eric Sibony
Incomplete rankings on a set of items are orderings of the form , with and . T…