no code implementations • 2 Oct 2023 • Hui Shi, Yann Traonmilin, J-F Aujol
Thanks to the maximum a posteriori Bayesian framework, such regularizer can be systematically linked with the distribution of the data.
1 code implementation • 28 Feb 2022 • Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol
We consider the problem of recovering off-the-grid spikes from Fourier measurements.
no code implementations • 7 Dec 2021 • Yann Traonmilin, Rémi Gribonval, Samuel Vaiter
To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint.
no code implementations • 12 May 2020 • Yann Traonmilin, Jean-François Aujol, Arthur Leclaire
We propose a new algorithm for sparse spike estimation from Fourier measurements.
no code implementations • 17 Apr 2020 • Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper. The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task.
no code implementations • 22 Jun 2017 • Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task.
no code implementations • 27 Oct 2016 • Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval
We demonstrate empirically that CKM performs similarly to Lloyd-Max, for a sketch size proportional to the number of cen-troids times the ambient dimension, and independent of the size of the original dataset.
no code implementations • 30 Sep 2016 • Antoine Deleforge, Yann Traonmilin
We consider the problem of estimating the phases of K mixed complex signals from a multichannel observation, when the mixing matrix and signal magnitudes are known.