Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions

arXiv:2606.30310 · stat.ML, cs.LG · Submitted 2026-06-29 · Read on arXiv

Christophe Vauthier, Quentin Mérigot, Anna Korba

stat.ML, cs.LG

Submitted: 2026-06-29

Code: https://github.com/UT-Austin-Data-Science-Group/AmortizedSW

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Wasserstein Distance — 53× in this paper · explained in 6 episodes
• Sliced Wasserstein — 26× in this paper
• Differentially Private — 19× in this paper · explained in 3 episodes
• Clustering — 17× in this paper · explained in 2 episodes
• Federated Learning — 17× in this paper · explained in 19 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Differential Privacy — 13× in this paper · explained in 5 episodes
• Generative Modeling — 11× in this paper · explained in 3 episodes
• Optimal Transport — 10× in this paper · explained in 5 episodes
• Generator — 7× in this paper · explained in 2 episodes
• Spectral Clustering — 7× in this paper · explained in 3 episodes
• Core — 6× in this paper · explained in 2 episodes
• Hyperparameters — 5× in this paper · explained in 2 episodes
• Papers — 5× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 3 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Editor — 2× in this paper · explained in 2 episodes
• Generative Adversarial Network — 2× in this paper
• Gradient Flow — 2× in this paper · explained in 2 episodes
• Institutional Review Board — 2× in this paper
• Robustness — 2× in this paper · explained in 6 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fixed Point — 1× in this paper · explained in 2 episodes
• Fréchet Inception Distance (FID) — 1× in this paper · explained in 2 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• IEEE International Conference on Computer Vision — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Monte Carlo Sampling — 1× in this paper · explained in 2 episodes
• Mutual Information — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 4 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Orthogonal Projection — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Variance Reduction — 1× in this paper · explained in 2 episodes

Sources

• Federated Learning with Personalization Layers

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