Sliced Orlicz-Wasserstein

arXiv:2609.32847 · stat.ML, cs.LG, math.ST, stat.CO, stat.TH · Submitted 2026-09-26 · Read on arXiv

stat.ML, cs.LG, math.ST, stat.CO, stat.TH

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/khainb/SOW

Terminology

• Wasserstein Distance — 36× in this paper · explained in 4 episodes
• Optimal Transport — 9× in this paper · explained in 5 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Sliced Orlicz-Wasserstein — 6× in this paper
• Generative Models — 4× in this paper · explained in 2 episodes
• Minimax optimality — 4× in this paper · explained in 2 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Homogeneity — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• ACM Transactions on Graphics — 2× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Additivity — 2× in this paper · explained in 1 episode
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sample Complexity — 2× in this paper · explained in 15 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• MIND: Monge Inception Distance for Generative Models Evaluation
• Sinkhorn-Drifting Generative Models
• Optimal Transport for Machine Learners
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• A Practical Introduction to Kernel Discrepancies: MMD, HSIC & KSD

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