Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds
Fuente:
arXiv
Saved in:
| Main Authors: | Xu, Haoshu, Li, Hongzhe |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
by: Xu, Haoshu, et al.
Published: (2025)
by: Xu, Haoshu, et al.
Published: (2025)
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
by: Jiang, Peiwen, et al.
Published: (2026)
by: Jiang, Peiwen, et al.
Published: (2026)
Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026)
by: Nguyen, Duc Toan, et al.
Published: (2026)
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
by: Gu, Hyemin, et al.
Published: (2024)
by: Gu, Hyemin, et al.
Published: (2024)
On metric choice in dimension reduction for Fréchet regression
by: Soale, Abdul-Nasah, et al.
Published: (2024)
by: Soale, Abdul-Nasah, et al.
Published: (2024)
Knowledge-Guided Wasserstein Distributionally Robust Optimization
by: Wang, Zitao, et al.
Published: (2025)
by: Wang, Zitao, et al.
Published: (2025)
Centered plug-in estimation of Wasserstein distances
by: Papp, Tamás P., et al.
Published: (2022)
by: Papp, Tamás P., et al.
Published: (2022)
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
by: Luo, Hengrui, et al.
Published: (2026)
by: Luo, Hengrui, et al.
Published: (2026)
Fréchet random forests for metric space valued regression with non euclidean predictors
by: Capitaine, Louis, et al.
Published: (2019)
by: Capitaine, Louis, et al.
Published: (2019)
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)
by: Luu, Hoang Phuc Hau, et al.
Published: (2024)
On the Wasserstein Geodesic Principal Component Analysis of probability measures
by: Vesseron, Nina, et al.
Published: (2025)
by: Vesseron, Nina, et al.
Published: (2025)
Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport
by: Wilson, Michael, et al.
Published: (2024)
by: Wilson, Michael, et al.
Published: (2024)
Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space
by: Feitelberg, Jacob, et al.
Published: (2024)
by: Feitelberg, Jacob, et al.
Published: (2024)
Barycenter Estimation of Positive Semi-Definite Matrices with Bures-Wasserstein Distance
by: Zheng, Jingyi, et al.
Published: (2023)
by: Zheng, Jingyi, et al.
Published: (2023)
Bures-Wasserstein Means of Graphs
by: Haasler, Isabel, et al.
Published: (2023)
by: Haasler, Isabel, et al.
Published: (2023)
Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)
by: Matsubara, Takuo
Published: (2024)
Network Learning with Semi-relaxed Gromov-Wasserstein
by: Dufour, Charles, et al.
Published: (2026)
by: Dufour, Charles, et al.
Published: (2026)
Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
by: Sharrock, Louis
Published: (2026)
by: Sharrock, Louis
Published: (2026)
Fréchet regression of multivariate distributions with nonparanormal transport
by: Park, Junyoung, et al.
Published: (2026)
by: Park, Junyoung, et al.
Published: (2026)
Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
by: You, Kisung, et al.
Published: (2025)
by: You, Kisung, et al.
Published: (2025)
Minimax-Optimal Two-Sample Test with Sliced Wasserstein
by: Tran, Binh Thuan, et al.
Published: (2025)
by: Tran, Binh Thuan, et al.
Published: (2025)
An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2024)
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2024)
Deep Fréchet Regression
by: Iao, Su I, et al.
Published: (2024)
by: Iao, Su I, et al.
Published: (2024)
Fréchet Geodesic Boosting
by: Zhou, Yidong, et al.
Published: (2025)
by: Zhou, Yidong, et al.
Published: (2025)
Bures-Wasserstein Flow Matching for Graph Generation
by: Jiang, Keyue, et al.
Published: (2025)
by: Jiang, Keyue, et al.
Published: (2025)
Minimum Wasserstein distance estimator under covariate shift: closed-form, super-efficiency and irregularity
by: Lang, Junjun, et al.
Published: (2026)
by: Lang, Junjun, et al.
Published: (2026)
Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks
by: Qiu, Yixuan, et al.
Published: (2024)
by: Qiu, Yixuan, et al.
Published: (2024)
Barycentric model aggregation in the Wasserstein space of distributions and a variational approach to consistency
by: Androulakis, Emmanouil, et al.
Published: (2025)
by: Androulakis, Emmanouil, et al.
Published: (2025)
Statistical Inference for Bures-Wasserstein Flows
by: Santoro, Leonardo V., et al.
Published: (2023)
by: Santoro, Leonardo V., et al.
Published: (2023)
Learning covariate importance for matching in policy-relevant observational research
by: Zhang, Hongzhe, et al.
Published: (2024)
by: Zhang, Hongzhe, et al.
Published: (2024)
Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry
by: Wang, Rui, et al.
Published: (2025)
by: Wang, Rui, et al.
Published: (2025)
On the potential benefits of entropic regularization for smoothing Wasserstein estimators
by: Bigot, Jérémie, et al.
Published: (2022)
by: Bigot, Jérémie, et al.
Published: (2022)
Wasserstein projection distance for fairness testing of regression models
by: Li, Wanxin, et al.
Published: (2025)
by: Li, Wanxin, et al.
Published: (2025)
DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses
by: Kim, Kyum, et al.
Published: (2025)
by: Kim, Kyum, et al.
Published: (2025)
Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
by: Xia, Mingtao, et al.
Published: (2024)
by: Xia, Mingtao, et al.
Published: (2024)
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy
by: Fazeli-Asl, Forough, et al.
Published: (2023)
by: Fazeli-Asl, Forough, et al.
Published: (2023)
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)
by: Kim, Youngseok, et al.
Published: (2022)
Personalizing black-box models for nonparametric regression with minimax optimality
by: Li, Sai, et al.
Published: (2026)
by: Li, Sai, et al.
Published: (2026)
Network-based Neighborhood regression
by: Zhen, Yaoming, et al.
Published: (2024)
by: Zhen, Yaoming, et al.
Published: (2024)
Progression: an extrapolation principle for regression
by: Buriticá, Gloria, et al.
Published: (2024)
by: Buriticá, Gloria, et al.
Published: (2024)
Similar Items
-
Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
by: Xu, Haoshu, et al.
Published: (2025) -
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
by: Jiang, Peiwen, et al.
Published: (2026) -
Fréchet Regression on the Bures-Wasserstein Manifold
by: Nguyen, Duc Toan, et al.
Published: (2026) -
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flows
by: Gu, Hyemin, et al.
Published: (2024) -
On metric choice in dimension reduction for Fréchet regression
by: Soale, Abdul-Nasah, et al.
Published: (2024)