Conformal Prediction using Conditional HistogramsDownload PDF

May 21, 2021 (edited Jan 21, 2022)NeurIPS 2021 SpotlightReaders: Everyone
  • Keywords: Conformal prediction, quantile regression, histograms, skewed data.
  • TL;DR: This paper develops and studies a novel conformal method to compute prediction intervals that automatically adapt to skewed data.
  • Abstract: 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 achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.
  • Supplementary Material: pdf
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  • Code: https://github.com/msesia/chr
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