Tag: gp
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Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches
Design-marginal calibration of Gaussian process predictive distributions: Bayesian and conformal approaches arXiv:2512.05611v1 Announce Type: new Abstract: We study the calibration of Gaussian process (GP) predictive distributions in the interpolation setting from a design-marginal perspective. Conditioning on the data and averaging over a design measure mu, we formalize mu-coverage for central intervals and mu-probabilistic calibration through…
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Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent arXiv:2508.15674v1 Announce Type: new Abstract: Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative regret upper bound of EI remains an open question. In this paper, we…
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Bayesian Optimization of Robustness Measures Using Randomized GP-UCB-based Algorithms under Input Uncertainty
Bayesian Optimization of Robustness Measures Using Randomized GP-UCB-based Algorithms under Input Uncertainty arXiv:2504.03172v1 Announce Type: new Abstract: Bayesian optimization based on Gaussian process upper confidence bound (GP-UCB) has a theoretical guarantee for optimizing black-box functions. Black-box functions often have input uncertainty, but even in this case, GP-UCB can be extended to optimize evaluation measures called…