EPICSCORE transforms any conformal score via a Bayesian posterior predictive CDF, preserving marginal coverage and, under a uniform-convergence assumption, yielding asymptotic conditional coverage.
Bayesian additive regression trees for probabilistic programming
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abstract
Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees' learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPL), i.e., we present BART as a primitive that can be used as a component of a probabilistic model rather than as a standalone model. Specifically, we introduce the Python library PyMC-BART, which works by extending PyMC, a library for probabilistic programming. We showcase a few examples of models that can be built using PyMC-BART, discuss recommendations for the selection of hyperparameters, and finally, we close with limitations of our implementation and future directions for improvement.
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stat.ML 1years
2025 1verdicts
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Epistemic Uncertainty in Conformal Scores: A Unified Approach
EPICSCORE transforms any conformal score via a Bayesian posterior predictive CDF, preserving marginal coverage and, under a uniform-convergence assumption, yielding asymptotic conditional coverage.