13 citations · 20 across the 4 of their papers we have counts for
6 papers · 1 filter
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Several fields in science, from genomics to neuroimaging, require monitoring populations (measures) that evolve with time. These complex datasets, describing dynamics with both tim…
Debiased Sinkhorn barycenters
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Entropy regularization in optimal transport (OT) has been the driver of many recent interests for Wasserstein metrics and barycenters in machine learning. It allows to keep the app…
Spatio-Temporal Alignments: Optimal transport through space and time
Hicham Janati, Marco Cuturi, Alexandre Gramfort
Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account th…
Multi-subject MEG/EEG source imaging with sparse multi-task regression
Hicham Janati, Thomas Bazeille, Bertrand Thirion +2
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Estimating the loca…
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Hicham Janati, Thomas Bazeille, Bertrand Thirion +2
Magnetoencephalography (MEG) and electroencephalogra-phy (EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity. Inferring the…
Wasserstein regularization for sparse multi-task regression
Hicham Janati, Marco Cuturi, Alexandre Gramfort
We focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space…