Distribution Regression with Sliced Wasserstein Kernels

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Distribution Regression with Sliced Wasserstein Kernels

8 February 2022

Dimitri Meunier

Massimiliano Pontil

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Papers citing "Distribution Regression with Sliced Wasserstein Kernels"

11 / 11 papers shown

Title
Distributional encoding for Gaussian process regression with qualitative inputs Sébastien Da Veiga UQCV 95 0 0 05 Jun 2025
Improved learning theory for kernel distribution regression with two-stage sampling François Bachoc Louis Bethune Alberto González Sanz Jean-Michel Loubes 145 2 0 28 Jan 2025
Wasserstein Spatial Depth François Bachoc Alberto González Sanz Jean-Michel Loubes Yisha Yao 89 1 0 16 Nov 2024
Learning to Embed Distributions via Maximum Kernel Entropy Oleksii Kachaiev Stefano Recanatesi OOD 120 0 0 01 Aug 2024
Uncertainty quantification in metric spaces Gábor Lugosi Marcos Matabuena 137 5 0 08 May 2024
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels Raphael Carpintero Perez Sébastien da Veiga Josselin Garnier B. Staber 107 6 0 06 Feb 2024
Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates Rémi Leluc Aymeric Dieuleveut Franccois Portier Johan Segers Aigerim Zhuman 78 7 0 02 Feb 2024
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals Clément Bonet Benoit Malézieux A. Rakotomamonjy Lucas Drumetz Thomas Moreau M. Kowalski Nicolas Courty 96 16 0 10 Mar 2023
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 85 12 0 20 Nov 2022
Gaussian Processes on Distributions based on Regularized Optimal Transport François Bachoc Louis Bethune Alberto González Sanz Jean-Michel Loubes GP OT 66 8 0 12 Oct 2022
Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression Q. Zhang Bing Li Lingzhou Xue 82 12 0 11 Jul 2022