研究人员开发了一种新的半监督核双样本检验，旨在利用丰富的无标签协变量数据。该方法旨在通过纳入协变量来提高性能，而协变量是标准检验通常会忽略的。所提出的方法确保了检验统计量的渐近正态性，简化了校准，并实现了比不使用协变量的现有核检验更高的渐近功效。 AI
影响 引入了一种新颖的双样本检验统计方法，可以增强机器学习模型的评估和开发。
排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种新的统计方法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.01775v1 Announce Type: new Abstract: We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which has the potential to significantly boost performance. …
We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which has the potential to significantly boost performance. However, incorporating covariates potentially br…