基于黑盒预测的最优推断
Optimal Inference with Black-box Predictions
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中文总结 AI 辅助
本研究针对高维高斯序列模型，刻画黑盒预测推断的信息论极限，开发适配预测未知准确率且利用预测强对齐的实用假设检验方法，填补了黑盒预测推断领域缺乏统一原则的缺口。
中文摘要 AI 辅助
强大的黑盒预测模型推动了诸多将观测数据与预测结果结合以开展有效统计推断的方案。尽管取得了这些进展，该领域仍缺乏统一原则来解释假设检验应如何整合数据与预测结果，以同时满足有效性与高效性。本研究针对高维高斯序列模型解决这一缺口，刻画了黑盒预测推断的信息论极限（当预测准确率已知时），以及正交预测下（当准确率未知时）的对应极限。基于这些刻画，我们开发了实用的假设检验方法，该方法可适配预测的未知准确率，同时从预测间的强对齐中获益。
英文摘要
Powerful black-box predictive models have motivated many proposals for combining observed data with predictions to perform valid statistical inference. Despite this progress, the field lacks a unifying principle that explains how hypothesis tests should integrate data and predictions in a way that is both valid and efficient. In this work, we address this gap in the high-dimensional Gaussian sequence model. We characterize the information-theoretic limits of inference with black-box predictions when their accuracies are known and, for orthogonal predictions, when they are unknown. Building on these characterizations, we develop practical hypothesis tests that adapt to the unknown accuracies of the predictions while benefiting from strong alignment among them.
