华盛顿大学于孟鑫助理教授：现代机器学习预测的不确定性量化
报告摘要
Quantifying the uncertainty of black-box machine learning predictions is a core problem in modern statistics. Methods for predictive inference have been developed under a variety of assumptions, often -- for instance, in standard conformal prediction
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