Conformal Bayesian ComputationDownload PDF

21 May 2021, 20:46 (edited 17 Dec 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Conformal prediction, Bayesian Inference, Calibration
  • TL;DR: We compute conformal predictive intervals from Bayesian models using add-one-in importance sampling.
  • Abstract: We develop scalable methods for producing conformal Bayesian predictive intervals with finite sample calibration guarantees. Bayesian posterior predictive distributions, $p(y \mid x)$, characterize subjective beliefs on outcomes of interest, $y$, conditional on predictors, $x$. Bayesian prediction is well-calibrated when the model is true, but the predictive intervals may exhibit poor empirical coverage when the model is misspecified, under the so called ${\cal{M}}$-open perspective. In contrast, conformal inference provides finite sample frequentist guarantees on predictive confidence intervals without the requirement of model fidelity. Using 'add-one-in' importance sampling, we show that conformal Bayesian predictive intervals are efficiently obtained from re-weighted posterior samples of model parameters. Our approach contrasts with existing conformal methods that require expensive refitting of models or data-splitting to achieve computational efficiency. We demonstrate the utility on a range of examples including extensions to partially exchangeable settings such as hierarchical models.
  • Supplementary Material: pdf
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  • Code: https://github.com/edfong/conformal_bayes
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