博士生讨论班2025[07]
作者:
时间:2025-03-28
阅读量:42次
  • 演讲人: 孙荣忆
  • 时间:2025年4月1日14:00
  • 地点:浙江大学紫金港校区行政楼1417报告厅

报告文章:Conformal Prediction using Conditional Histograms (Matteo Sesia&Yaniv Romano)
摘要:This paper develops a conformal method to compute prediction intervals for non parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while asymptotically achiev ing conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved perfor mance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.