Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach
arXiv:2507.12843v3 Announce Type: replace Abstract: Are two distributions close to each other with statistical significance? Distribution closeness testing (DCT) formalizes this question by testing whether the distance between a distribution pair is at least epsilon-far. Existing DCT methods mainly measure discrepancies between distribution pairs defined on discrete spaces, for example using total variation, which limits their application to complex data such as images. To extend DCT to more types of data, a natural idea is to introduce maximum mean discrepancy (MMD), a powerful measure of dis
The paper addresses a current limitation in distribution closeness testing within AI/ML research by extending methods to complex data types, spurred by the continuous advancement in machine learning applications.
Improving the ability to statistically compare complex datasets is crucial for validating machine learning models, ensuring data quality, and developing deployable AI systems, impacting fields from computer vision to generative AI.
The proposed kernel distributional closeness testing approach allows for more robust statistical comparison of complex, high-dimensional datasets, moving beyond limitations of discrete space methods.
- · AI/ML researchers
- · Data scientists
- · Developers of generative AI
- · Quality assurance in ML
- · Methods relying on simple distance metrics
More rigorous statistical validation of model outputs and synthetic data generation will become possible.
Increased trust and reliability in AI systems that depend on accurately comparing complex data distributions, especially in safety-critical applications.
Acceleration of research into novel ML architectures and data generation techniques due to improved validation tools.
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Read at arXiv cs.LG