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Conformal Prediction using Conditional Histograms
Matteo Sesia · Yaniv Romano

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @

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.

Author Information

Matteo Sesia (University of Southern California)

Matteo Sesia is an assistant professor in the Department of Data Sciences and Operations, at the University of Southern California, Marshall School of Business.

Yaniv Romano (Technion---Israel Institute of Technology)

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