Cover Image

Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wilson, Michael, Needham, Tom, Srivastava, Anuj
Format:	Preprint
Published:	2024
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Network Learning with Semi-relaxed Gromov-Wasserstein
by: Dufour, Charles, et al.
Published: (2026)

The Z-Gromov-Wasserstein Distance
by: Bauer, Martin, et al.
Published: (2024)

Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild
by: Im, Jiin, et al.
Published: (2026)

Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
by: Sharrock, Louis
Published: (2026)

Entropic Riemannian Neural Optimal Transport
by: Micheli, Alessandro, et al.
Published: (2026)

Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds
by: Xu, Haoshu, et al.
Published: (2024)

Minimax-Optimal Two-Sample Test with Sliced Wasserstein
by: Tran, Binh Thuan, et al.
Published: (2025)

Optimal Transport on Categorical Data for Counterfactuals using Compositional Data and Dirichlet Transport
by: Machado, Agathe Fernandes, et al.
Published: (2025)

Streaming Sliced Optimal Transport
by: Nguyen, Khai
Published: (2025)

Fused Partial Gromov-Wasserstein for Structured Objects
by: Bai, Yikun, et al.
Published: (2025)

Metric properties of partial and robust Gromov-Wasserstein distances
by: Chhoa, Jannatul, et al.
Published: (2024)

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
by: Park, Soojin, et al.
Published: (2025)

On Model-Based Clustering With Entropic Optimal Transport
by: Mena, Gonzalo
Published: (2026)

Knowledge-Guided Wasserstein Distributionally Robust Optimization
by: Wang, Zitao, et al.
Published: (2025)

Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
by: Huang, Xinmeng, et al.
Published: (2023)

Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches
by: Aka, Samira, et al.
Published: (2026)

A projected nonlinear state-space model for forecasting time series signals
by: Donner, Christian, et al.
Published: (2023)

Minimax Optimal Estimation of Transport-Growth Pairs in Unbalanced Optimal Transport
by: Ponnoprat, Donlapark, et al.
Published: (2026)

On the Wasserstein Geodesic Principal Component Analysis of probability measures
by: Vesseron, Nina, et al.
Published: (2025)

Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
by: Matabuena, Marcos, et al.
Published: (2024)

Efficient Generative Modeling via Penalized Optimal Transport Network
by: Lu, Wenhui Sophia, et al.
Published: (2024)

Reversible Gromov-Monge Sampler for Simulation-Based Inference
by: Hur, YoonHaeng, et al.
Published: (2021)

Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
by: Feitelberg, Jacob, et al.
Published: (2024)

Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
by: Luo, Hengrui, et al.
Published: (2026)

Functional Analysis of Variance for Association Studies
by: Vsevolozhskaya, Olga A., et al.
Published: (2025)

Centered plug-in estimation of Wasserstein distances
by: Papp, Tamás P., et al.
Published: (2022)

Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
by: Gu, Hyemin, et al.
Published: (2024)

Automatic Outlier Rectification via Optimal Transport
by: Blanchet, Jose, et al.
Published: (2024)

An Introduction to Sliced Optimal Transport
by: Nguyen, Khai
Published: (2025)

Causal Discovery in Structural VAR Models Under Equal Noise Variance
by: HasanAbadi, SeyedSina Seyedi, et al.
Published: (2026)

Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
by: Yu, Lu, et al.
Published: (2022)

Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)

Fused Gromov-Wasserstein Distance with Feature Selection
by: Lee, Harlin, et al.
Published: (2026)

Optimal Cross-Validation for Sparse Linear Regression
by: Cory-Wright, Ryan, et al.
Published: (2023)

A Novel Sliced Fused Gromov-Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)

Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data
by: Zhang, Anru, et al.
Published: (2018)

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects
by: Fisher, Aaron
Published: (2023)

Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
by: You, Kisung, et al.
Published: (2025)

Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
by: Agarwal, Medha, et al.
Published: (2026)

Stability Bounds for Smooth Optimal Transport Maps and their Statistical Implications
by: Balakrishnan, Sivaraman, et al.
Published: (2025)