RoSF-BO: Robust Surrogate Fitting via Low-Noise Data Augmentation for Bayesian Optimization

Bayesian optimization (BO) relies on surrogate models fitted to limited observations, making its performance particularly sensitive to observation noise. Existing noise-handling approaches mainly focus on surrogate modeling or acquisition design, while observation-level noise mitigation remains relatively underexplored. To address this issue, we propose RoSF-BO, an observation-level data augmentation framework that improves the training data used for surrogate fitting without additional expensive evaluations or discarding original observations. RoSF-BO exploits local functional relationships among evaluated solutions to identify observations with complementary deviations and generates additional training samples with reduced noise interference through residual-cancellation interpolation. We further provide a theoretical analysis to explain the noise-reduction effect of the generated samples. Extensive experiments on static datasets, noisy single-objective BO, and noisy multi-objective BO demonstrate that RoSF-BO consistently improves data quality and translates these improvements into better optimization performance across different BO methods and observation distributions. Overall, RoSF-BO provides a new observation-level perspective for noise handling in Bayesian optimization and complements existing surrogate- and acquisition-level approaches while preserving the standard BO acquisition and evaluation procedures.