Papers › Bayesian Optimization with Conformal Prediction Sets

Bayesian Optimization with Conformal Prediction Sets

22 Oct 2022arXiv:2210.12496archive 2025-07-28

Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson

Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization. Bayesian optimization selects decisions (i.e. objective function queries) with maximal expected utility with respect to the posterior distribution of a Bayesian model, which quantifies reducible, epistemic uncertainty about query outcomes. In practice, subjectively implausible outcomes can occur regularly for two reasons: 1) model misspecification and 2) covariate shift. Conformal prediction is an uncertainty quantification method with coverage guarantees even for misspecified models and a simple mechanism to correct for covariate shift. We propose conformal Bayesian optimization, which directs queries towards regions of search space where the model predictions have guaranteed validity, and investigate its behavior on a suite of black-box optimization tasks and tabular ranking tasks. In many cases we find that query coverage can be significantly improved without harming sample-efficiency.

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baseline_candidate_split samuelstanton/conformal-bayesopt/scripts/tab_bandits.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 9b080ad950cbb24b · report
get_src_df samuelstanton/conformal-bayesopt/scripts/tab_bandits.py official repository ran · our draft was wrong Apache-2.0 (permissive) · be3754a85ebaa20c · report
softplus samuelstanton/conformal-bayesopt/conformalbo/acquisition/monte_carlo.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 3cdb235923bd8c57 · report

Tasks

Active LearningBayesian OptimizationConformal PredictionDecision MakingDecision Making Under UncertaintyPredictionPrediction IntervalsUncertainty Quantification

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