Robust Human-AI Collaborative Bayesian Optimization under Spatially Varying Expert Reliability

Human knowledge can accelerate Bayesian optimization (BO), but such knowledge is typically qualitative, noisy, biased, and, more importantly, spatially varying in reliability. Although this spatial heterogeneity better reflects practical expertise, it is largely overlooked by existing human-augmented BO methods, which control human influence globally or according to a prespecified schedule. To address this limitation, we propose a novel trust-aware human-AI collaborative BO framework that adaptively modulates the contribution of human knowledge across the search space. Specifically, input-dependent trust is estimated from local human-objective ranking agreement using localized conformal calibration and then incorporated into the multi-fidelity kernel. Furthermore, we establish two theoretical guarantees: a calibration guarantee that controls the long-run frequency of assigning high trust to human guidance inconsistent with objective observations, and a sublinear cumulative regret bound for the resulting trust-aware BO procedure. Experiments with spatially varying emulated experts, as well as two real-world experiments, consistently demonstrate that the proposed method outperforms standard and human-augmented BO baselines under spatially varying expert reliability while remaining robust to misleading guidance.