1 code implementation • 10 Oct 2022 • Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux

In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.

no code implementations • 4 Oct 2022 • Tanguy Marchand, Boris Muzellec, Constance Beguier, Jean Ogier du Terrail, Mathieu Andreux

The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning.

no code implementations • 3 Dec 2021 • Boris Muzellec, Adrien Vacher, Francis Bach, François-Xavier Vialard, Alessandro Rudi

It was recently shown that under smoothness conditions, the squared Wasserstein distance between two distributions could be efficiently computed with appealing statistical error upper bounds.

1 code implementation • 22 Nov 2021 • Boris Muzellec, Francis Bach, Alessandro Rudi

Shape constraints such as positive semi-definiteness (PSD) for matrices or convexity for functions play a central role in many applications in machine learning and sciences, including metric learning, optimal transport, and economics.

1 code implementation • 18 Jun 2021 • Boris Muzellec, Francis Bach, Alessandro Rudi

Kernel mean embeddings are a popular tool that consists in representing probability measures by their infinite-dimensional mean embeddings in a reproducing kernel Hilbert space.

no code implementations • 13 Jan 2021 • Adrien Vacher, Boris Muzellec, Alessandro Rudi, Francis Bach, Francois-Xavier Vialard

It is well-known that plug-in statistical estimation of optimal transport suffers from the curse of dimensionality.

Statistics Theory Optimization and Control Statistics Theory 62G05

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

no code implementations • 29 Feb 2020 • Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki

In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space.

1 code implementation • ICML 2020 • Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi

Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.

1 code implementation • NeurIPS 2019 • Boris Muzellec, Marco Cuturi

A popular approach to avoid this curse is to project input measures on lower-dimensional subspaces (1D lines in the case of sliced Wasserstein distances), solve the OT problem between these reduced measures, and settle for the Wasserstein distance between these reductions, rather than that between the original measures.

2 code implementations • NeurIPS 2018 • Boris Muzellec, Marco Cuturi

We propose in this work an extension of that approach, which consists in embedding objects as elliptical probability distributions, namely distributions whose densities have elliptical level sets.

no code implementations • 22 Sep 2016 • Frank Nielsen, Boris Muzellec, Richard Nock

We consider the supervised classification problem of machine learning in Cayley-Klein projective geometries: We show how to learn a curved Mahalanobis metric distance corresponding to either the hyperbolic geometry or the elliptic geometry using the Large Margin Nearest Neighbor (LMNN) framework.

1 code implementation • 15 Sep 2016 • Boris Muzellec, Richard Nock, Giorgio Patrini, Frank Nielsen

We also present the first application of optimal transport to the problem of ecological inference, that is, the reconstruction of joint distributions from their marginals, a problem of large interest in the social sciences.

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