<ul><li>Bayesian optimization (BO) is a sequential approach for optimizing black-box objective functions using zeroth-order noisy observations.</li><li>To address the issue of model misspecification in BO, a localized online conformal prediction-based Bayesian optimization (LOCBO) algorithm is introduced.</li><li>LOCBO corrects the likelihood of the Gaussian process (GP) model through localized online conformal prediction, resulting in a calibrated posterior distribution on the objective function.</li><li>Experiments on synthetic and real-world optimization tasks confirm that LOCBO outperforms state-of-the-art BO algorithms in the presence of model misspecification.</li></ul>

Robust Bayesian Optimization via Localized Online Conformal Prediction

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