AI RESEARCH
Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
arXiv CS.LG
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ArXi:2405.14657v2 Announce Type: replace Preferential Bayesian optimization (PBO) learns latent utilities from pairwise comparisons, but most existing methods assume homoscedastic comparison noise. This is inadequate in human-in-the-loop settings, where a user may compare some designs reliably and others only hesitantly. We propose a heteroscedastic noise model for PBO: before optimization, the user provides a small set of reliable examples, called anchors, and a kernel density estimator (KDE) turns these anchors into an input-dependent map of user uncertainty.