113 citations · 265 across the 15 of their papers we have counts for
17 papers
survivalGPU: Scalable Survival Analysis for R and Python
Antoine Poirot-Bourdain, Alexis Van Straaten, Emmanuel Bacry +2
This paper introduces survivalGPU, a high-performance Python and R package designed to accelerate survival analysis computations using graphics processing units (GPUs). While the C…
A Unified Dual Method for Matching Problems
Guillaume Houry, Ferdinand Genans, Jean Feydy +1
Matching problems are ubiquitous in data science as they enable the alignment of structured objects and distributions. While existing solvers are often tailored to specific matchin…
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2
Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, order…
Gromov-Wasserstein at Scale, Beyond Squared Norms
Guillaume Houry, Jean Feydy, François-Xavier Vialard
A fundamental challenge in data science is to match disparate point sets with each other. While optimal transport efficiently minimizes point displacements under a bijectivity cons…
Untangling Vascular Trees for Surgery and Interventional Radiology
Guillaume Houry, Tom Boeken, Stéphanie Allassonnière +1
The diffusion of minimally invasive, endovascular interventions motivates the development of visualization methods for complex vascular networks. We propose a planar representation…
Sinkhorn Normalization of Diffusion Kernels
Nathan Kessler, Robin Magnet, Jean Feydy
Smoothing a signal based on local neighborhoods is a core operation in machine learning and geometry processing. On well-structured domains such as vector spaces and manifolds, the…
Fast Large Deformation Matching with the Energy Distance Kernel
Siwan Boufadene, François-Xavier Vialard, Jean Feydy
We propose an efficient framework for point cloud and measure registration using bi-Lipschitz homeomorphisms, achieving O(n log n) complexity, where n is the number of points. By l…
Anisotropic power diagrams for polycrystal modelling: efficient generation of curved grains via optimal transport
Maciej Buze, Jean Feydy, Steven M. Roper +2
The microstructure of metals and foams can be effectively modelled with anisotropic power diagrams (APDs), which provide control over the shape of individual grains. One major obst…
Multicellular simulations with shape and volume constraints using optimal transport
Antoine Diez, Jean Feydy
Many living and physical systems such as cell aggregates, tissues or bacterial colonies behave as unconventional systems of particles that are strongly constrained by volume exclus…
Approximation and Structured Prediction with Sparse Wasserstein Barycenters
Minh-Hieu Do, Jean Feydy, Olga Mula
We develop a general theoretical and algorithmic framework for sparse approximation and structured prediction in with Wasserstein barycenters. The barycenters ar…
Accurate Point Cloud Registration with Robust Optimal Transport
Zhengyang Shen, Jean Feydy, Peirong Liu +4
This work investigates the use of robust optimal transport (OT) for shape matching. Specifically, we show that recent OT solvers improve both optimization-based and deep learning m…
Fast and Scalable Optimal Transport for Brain Tractograms
Jean Feydy, Pierre Roussillon, Alain Trouvé +1
We present a new multiscale algorithm for solving regularized Optimal Transport problems on the GPU, with a linear memory footprint. Relying on Sinkhorn divergences which are conve…
Kernel Operations on the GPU, with Autodiff, without Memory Overflows
Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès +2
The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices. KeOps allevi…
Sinkhorn Divergences for Unbalanced Optimal Transport
Thibault Séjourné, Jean Feydy, François-Xavier Vialard +2
Optimal transport induces the Earth Mover's (Wasserstein) distance between probability distributions, a geometric divergence that is relevant to a wide range of problems. Over the…
Collective Proposal Distributions for Nonlinear MCMC samplers: Mean-Field Theory and Fast Implementation
Grégoire Clarté, Antoine Diez, Jean Feydy
Over the last decades, various "non-linear" MCMC methods have arisen. While appealing for their convergence speed and efficiency, their practical implementation and theoretical stu…
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard +3
Comparing probability distributions is a fundamental problem in data sciences. Simple norms and divergences such as the total variation and the relative entropy only compare densit…
Optimal Transport for Diffeomorphic Registration
Jean Feydy, Benjamin Charlier, François-Xavier Vialard +1
This paper introduces the use of unbalanced optimal transport methods as a similarity measure for diffeomorphic matching of imaging data. The similarity measure is a key object in…