LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
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 abundant reference data, we learn reference-dependent representations that summarize salient structure of the reference distribution and provide in...
| Authors | Xunye Tian, Zhijian Zhou, Liuhua Peng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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