A new multi-round algorithm, MR-LPF, achieves regret of order sqrt(Gamma(T)T) up to log factors for preference-based Bayesian optimization with binary human feedback, removing the extra kernel-complexity and link-curvature factors present in prior bounds.
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Bayesian Optimization from Human Feedback: Near-Optimal Regret Bounds
A new multi-round algorithm, MR-LPF, achieves regret of order sqrt(Gamma(T)T) up to log factors for preference-based Bayesian optimization with binary human feedback, removing the extra kernel-complexity and link-curvature factors present in prior bounds.