AI RESEARCH
ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
arXiv CS.LG
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ArXi:2605.06454v1 Announce Type: new Bayesian optimization is widely used for hyperparameter optimization when model evaluations are expensive; however, noisy acquisition estimates can lead to unstable decisions. We identify acquisition estimation noise as a failure mode that was previously overlooked: even when the surrogate model and acquisition target are correctly specified, finite-sample Monte Carlo error can perturb acquisition values. This can, in turn, flip candidate rankings and lead to suboptimal BO decisions.