The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an
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Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
The paper introduces an anchor-based heteroscedastic noise model for PBO that maps user uncertainty via KDE on reliable examples, incorporates it into GP surrogates, and derives risk-averse acquisition functions including a risk-adjusted EUBO variant that preserves one-step Bayes-optimality up to an