Elicitation-Augmented Bayesian Optimization
It enables Bayesian optimization to leverage tacit human expertise via pairwise comparisons, addressing a practical bottleneck in human-in-the-loop optimization.
This work introduces a Bayesian optimization method that integrates pairwise comparison queries from a human expert with direct observations, using a cost-aware value-of-information acquisition function. The method improves sample efficiency when pairwise queries are cheap and matches standard BO performance otherwise.
Human-in-the-loop Bayesian optimization (HITL BO) methods utilize human expertise to improve the sample-efficiency of BO. Most HITL BO methods assume that a domain expert can quantify their knowledge, for instance by pinpointing query locations or specifying their prior beliefs about the location of the maximum as a probability distribution. However, since human expertise is often tacit and cannot be explicitly quantified, we consider a setting where domain knowledge of an expert is elicited via pairwise comparisons of designs. We interpret the expert's pairwise judgements as noisy evidence about the values of the observable objective function and develop a principled method for combining the information obtained via direct observations and pairwise queries. Specifically, we derive a cost-aware value-of-information acquisition function that balances direct observations against pairwise queries. The proposed method approaches the convex hull of the trajectories of the individual information sources: when pairwise queries are cheap it substantially improves sample-efficiency over observation-only BO, and when pairwise queries are costly or noisy, it recovers the performance of standard BO by relying on direct observations alone.