A nonparametric two-sample test using a parametric integral probability metric

arXiv:2606.16941 · stat.ML, cs.LG · Submitted 2026-06-15 · Read on arXiv

Yuha Park, Yongdai Kim

stat.ML, cs.LG

Submitted: 2026-06-15

Comments: 45 pages. Accepted for publication in Statistical Analysis and Data Mining

Code: https://github.com/rtang1997/Two_sample_test_adversarial

Project page: https://shiry.eecs.berkeley.edu/yearbooks

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

Terminology

• Wasserstein Distance — 9× in this paper · explained in 6 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Heavy-Tailed Distributions — 8× in this paper · explained in 2 episodes
• Distribution Shift — 5× in this paper · explained in 12 episodes
• Generalization — 4× in this paper · explained in 14 episodes
• Theoretical Guarantees — 4× in this paper · explained in 2 episodes
• Causal Inference — 3× in this paper · explained in 5 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Robustness — 3× in this paper · explained in 6 episodes
• Composition — 2× in this paper · explained in 2 episodes
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Differential Privacy — 2× in this paper · explained in 5 episodes
• Distribution Shifts — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Exchangeability — 2× in this paper · explained in 2 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Maximum Mean Discrepancy — 2× in this paper
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Quantization — 2× in this paper · explained in 15 episodes
• ReLU Network — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Clarity — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Inflation — 1× in this paper · explained in 2 episodes
• Maximum Mean Discrepancy (MMD) — 1× in this paper · explained in 3 episodes
• Minimax Optimality — 1× in this paper · explained in 3 episodes
• National Research Foundation of Korea — 1× in this paper
• Performance Gap — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

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