ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization

arXiv:2605.06454v1 Announce Type: cross Abstract: 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 ranking