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Boris Muzellec

6 accepted papers

2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

NeurIPS 2022accept

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable clients, each holding medium to large datasets, and i…

2022

SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning

NeurIPS 2022accept

The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning. In this paper, we investigate the problem of applying the YJ transformation in a cross-silo Federated Learning setting under privacy constr…

Cited by 9SourcePDFScholar
2020

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

NeurIPS 2020oral

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. On the other hand, the numerical resolution of OT proble…

Cited by 101SourcePDFScholar
2020

Missing Data Imputation using Optimal Transport

ICML 2020poster

Missing data is a crucial issue when applying machine learning algorithms to real-world datasets. Starting from the simple assumption that two batches extracted randomly from the same dataset should share the same distribution, we leverage optimal transport distances to quantify that criterion and t…

2019

Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections

NeurIPS 2019poster

Computing optimal transport (OT) between measures in high dimensions is doomed by the curse of dimensionality. 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…

2018

Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions

NeurIPS 2018poster

Embedding complex objects as vectors in low dimensional spaces is a longstanding problem in machine learning. 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…