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pro vyhledávání: '"Lehéricy, Luc"'
Recent advances have demonstrated the possibility of solving the deconvolution problem without prior knowledge of the noise distribution. In this paper, we study the repeated measurements model, where information is derived from multiple measurements
Externí odkaz:
http://arxiv.org/abs/2409.02014
We prove oracle inequalities for a penalized log-likelihood criterion that hold even if the data are not independent and not stationary, based on a martingale approach. The assumptions are checked for various contexts: density estimation with indepen
Externí odkaz:
http://arxiv.org/abs/2405.10582
When fitting the learning data of an individual to algorithm-like learning models, the observations are so dependent and non-stationary that one may wonder what the classical Maximum Likelihood Estimator (MLE) could do, even if it is the usual tool a
Externí odkaz:
http://arxiv.org/abs/2305.06660
We consider noisy observations of a distribution with unknown support. In the deconvolution model, it has been proved recently [19] that, under very mild assumptions, it is possible to solve the deconvolution problem without knowing the noise distrib
Externí odkaz:
http://arxiv.org/abs/2304.09452
Autor:
Lehéricy, Luc, Touron, Augustin
A hidden Markov model with trends is a hidden Markov model whose emission distributions are translated by a trend that depends on the current hidden state and on the current time. Contrary to standard hidden Markov models, such processes are not homo
Externí odkaz:
http://arxiv.org/abs/2112.08731
Autor:
Hälvä, Hermanni, Corff, Sylvain Le, Lehéricy, Luc, So, Jonathan, Zhu, Yongjie, Gassiat, Elisabeth, Hyvarinen, Aapo
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to extend the identifiability theory of deep generative models for a very
Externí odkaz:
http://arxiv.org/abs/2106.09620
We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the ne
Externí odkaz:
http://arxiv.org/abs/2102.08023
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 assumption is made on the noise distribution and no samples are availa
Externí odkaz:
http://arxiv.org/abs/2006.14226
Autor:
Lehéricy, Luc
Dans cette thèse, j'étudie les propriétés théoriques des modèles de Markov cachés non paramétriques. Le choix de modèles non paramétriques permet d'éviter les pertes de performance liées à un mauvais choix de paramétrisation, d'où un r
Externí odkaz:
http://www.theses.fr/2018SACLS550/document
The pair-matching problem appears in many applications where one wants to discover good matches between pairs of entities or individuals. Formally, the set of individuals is represented by the nodes of a graph where the edges, unobserved at first, re
Externí odkaz:
http://arxiv.org/abs/1905.07342