arXiv:2606.08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-based feature representations). Such methods typically rely on data splitting to decouple learning from testing and control type I error. However, this paradigm is ill-suited to few-shot settings with severe sample-size imbalance: abundant reference samples are available, while only a handful of query samples arrive. In this paper, we show how this imbalance can be leveraged constructively. Using
This paper addresses a fundamental challenge in two-sample testing, particularly relevant as AI systems increasingly operate in data-scarce or imbalanced environments, pushing the boundaries of what is possible with limited data.
Improved capabilities in few-shot learning and robust statistical testing with imbalanced data will enhance the reliability and applicability of AI in critical sectors where data collection is difficult or expensive.
The ability to learn effectively from 'reference-only samples' and under 'size asymmetry' in two-sample testing fundamentally changes how machine learning algorithms can be applied to real-world scenarios with limited query data.
- · AI researchers
- · Healthcare diagnostics
- · Industrial anomaly detection
- · AI startup specializing in data-poor environments
- · Traditional data-intensive ML approaches
- · Companies with abundant but underleveraged reference data
More robust and effective two-sample testing even when query samples are scarce.
Accelerated development and deployment of AI applications in domains with inherent data imbalance, such as rare disease detection or novel material discovery.
Reduced barriers to entry for AI solutions in niche markets where comprehensive datasets are impractical or impossible to obtain, creating new economic opportunities.
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Read at arXiv cs.LG