Search Results for author: Hicham Janati

Found 11 papers, 5 papers with code

Fast kernel half-space depth for data with non-convex supports

no code implementations21 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.

Anomaly Detection Descriptive

Unbalanced CO-Optimal Transport

no code implementations30 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.

Domain Adaptation

Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments

1 code implementation11 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.

Dynamic Time Warping

Factored couplings in multi-marginal optimal transport via difference of convex programming

no code implementations1 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.

Information Retrieval Retrieval +1

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

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.

Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form

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

Debiased Sinkhorn barycenters

3 code implementations ICML 2020 Hicham Janati, Marco Cuturi, Alexandre Gramfort

However, entropy brings some inherent smoothing bias, resulting for example in blurred barycenters.

Spatio-Temporal Alignments: Optimal transport through space and time

2 code implementations9 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).

Dynamic Time Warping Time Series +1

Multi-subject MEG/EEG source imaging with sparse multi-task regression

no code implementations3 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.

EEG Electroencephalogram (EEG) +2

Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates

no code implementations13 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.

EEG Electroencephalogram (EEG)

Wasserstein regularization for sparse multi-task regression

1 code implementation20 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.

regression

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