Elicitation-Augmented Bayesian Optimization

Human-in-the-loop Bayesian optimization (HITL BO) methods utilize human expertise to improve the cost-efficiency of BO. Most of the HITL BO methods assume that an expert can explicitly quantify their domain knowledge, for instance by pinpointing query locations or specifying their prior beliefs about the location of the maximum as a probability distribution. However, domain knowledge is often tacit and can be expressed only implicitly. We take this into account by eliciting the expert's domain knowledge through pairwise comparisons of designs. We interpret the expert's pairwise judgements as noisy evidence about their mental model, and couple this model with a surrogate model of the objective function via a learned correlation. We develop a principled cost-aware method for combining pairwise comparisons with direct observations. The proposed method can adapt to expert bias by devaluing incorrect information: when pairwise queries are cheap and reliable, it substantially improves sample-efficiency over observation-only BO, and when queries are costly or unreliable, it devalues them.