41 citations · 214 across the 27 of their papers we have counts for
32 papers
Deconvolution of repeated measurements corrupted by unknown noise
Jérémie Capitao-Miniconi, Elisabeth Gassiat, Luc Lehéricy
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 measu…
Frontiers to the learning of nonparametric hidden Markov models
Kweku Abraham, Elisabeth Gassiat, Zacharie Naulet
Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the population densities. Identifi…
Support and distribution inference from noisy data
Jérémie Capitao-Miniconi, Elisabeth Gassiat, Luc Lehéricy
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 possibl…
Deconvolution of spherical data corrupted with unknown noise
Jérémie Capitao-Miniconi, Elisabeth Gassiat
We consider the deconvolution problem for densities supported on a -dimensional sphere with unknown center and unknown radius, in the situation where the distribution of the…
Fundamental limits for learning hidden Markov model parameters
Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat
We study the frontier between learnable and unlearnable hidden Markov models (HMMs). HMMs are flexible tools for clustering dependent data coming from unknown populations. The mode…
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy +4
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to…