no code implementations • 21 Dec 2023 • Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc, Hicham Janati
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics, anomaly detection, testing, etc.
no code implementations • 30 May 2022 • Quang Huy Tran, Hicham Janati, Nicolas Courty, Rémi Flamary, Ievgen Redko, Pinar Demetci, Ritambhara Singh
With this result in hand, we provide empirical evidence of this robustness for the challenging tasks of heterogeneous domain adaptation with and without varying proportions of classes and simultaneous alignment of samples and features across single-cell measurements.
1 code implementation • 11 Mar 2022 • Hicham Janati, Marco Cuturi, Alexandre Gramfort
These complex datasets, describing dynamics with both time and spatial components, pose new challenges for data analysis.
no code implementations • 1 Oct 2021 • Quang Huy Tran, Hicham Janati, Ievgen Redko, Rémi Flamary, Nicolas Courty
Optimal transport (OT) theory underlies many emerging machine learning (ML) methods nowadays solving a wide range of tasks such as generative modeling, transfer learning and information retrieval.
no code implementations • NeurIPS 2020 • Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi
Although optimal transport (OT) problems admit closed form solutions in a very few notable cases, e. g. in 1D or between Gaussians, these closed forms have proved extremely fecund for practitioners to define tools inspired from the OT geometry.
1 code implementation • NeurIPS 2020 • Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi
Although optimal transport (OT) problems admit closed form solutions in a very few notable cases, e. g. in 1D or between Gaussians, these closed forms have proved extremely fecund for practitioners to define tools inspired from the OT geometry.
Statistics Theory Statistics Theory
3 code implementations • ICML 2020 • Hicham Janati, Marco Cuturi, Alexandre Gramfort
However, entropy brings some inherent smoothing bias, resulting for example in blurred barycenters.
2 code implementations • 9 Oct 2019 • Hicham Janati, Marco Cuturi, Alexandre Gramfort
In this paper, we propose Spatio-Temporal Alignments (STA), a new differentiable formulation of DTW, in which spatial differences between time samples are accounted for using regularized optimal transport (OT).
no code implementations • 3 Oct 2019 • Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity.
no code implementations • 13 Feb 2019 • Hicham Janati, Thomas Bazeille, Bertrand Thirion, Marco Cuturi, Alexandre Gramfort
Inferring the location of the current sources that generated these magnetic fields is an ill-posed inverse problem known as source imaging.
1 code implementation • 20 May 2018 • Hicham Janati, Marco Cuturi, Alexandre Gramfort
We argue in this paper that these techniques fail to leverage the spatial information associated to regressors.