GRIBONVAL , Rémi

We consider the problem of recovering elements of a low-dimensional model from under-determined linear measurements. To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint. Given a model, we ask ourselves what is the ``best'' convex regularizer to perform its recovery. To answer this question,...

We consider the problem of recovering elements of a low-dimensional model from under-determined linear measurements. To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint. Given a model, we ask ourselves what is the ``best'' convex regularizer to perform its recovery. To answer this question,...

We consider the problem of recovering elements of a low-dimensional model from under-determined linear measurements. To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint. Given a model, we ask ourselves what is the "best" convex regularizer to perform its recovery. To answer this question,...

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...

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. A near-minimizer of the risk...

International audience

International audience

International audience

International audience

Identifying the location and spatial extent of several highly correlated and simultaneously active brain sources from electroencephalographic (EEG) recordings and extracting the corresponding brain signals is a challenging problem. In a recent comparison of source imaging techniques, the VB-SCCD algorithm, which exploits the sparsity of the...

The objective of brain source imaging consists in reconstructing the cerebral activity everywhere within the brain based on EEG or MEG measurements recorded on the scalp. This requires solving an ill-posed linear inverse problem. In order to restore identifiability, additional hypotheses need to be imposed on the source distribution, giving...

A number of application areas such as biomedical engineering require solving an underdetermined linear inverse problem. In such a case, it is necessary to make assumptions on the sources to restore identifiability. This problem is encountered in brain source imaging when identifying the source signals from noisy electroencephalographic or...