Local Preferential Bayesian Optimization Methods for High-Dimensional Problems
A new family of local Preferential Bayesian Optimization (PBO) methods has been introduced to address the scalability limitations of existing approaches in high-dimensional settings. Current PBO methods learn from pairwise human feedback rather than explicit objective functions, but struggle beyond low- and medium-dimensional problems due to global search strategies. The proposed local methods, which adapt trust-region and derivative-informed search to preference feedback, could improve real-world applications such as policy search and human-in-the-loop optimization.
Bayesian optimization (BO) is widely used for tuning expensive and noisy experiments, but typically requires an explicit objective function. Preferential Bayesian Optimization (PBO) sidesteps this by learning from pairwise human comparisons, making it suitable for settings where preferences are easier to elicit than numerical scores. However, existing PBO methods rely on global search strategies that become inefficient in high-dimensional or complex optimization landscapes. The authors introduce a family of local PBO methods that adapt trust-region approaches and derivative-informed local search — leveraging first- and second-order derivatives of a Laplace-approximated Gaussian process posterior — to the preferential feedback setting. Benchmarks on GP sample paths, standard optimization functions, and policy-search tasks demonstrate that these local methods substantially reduce cumulative regret compared to global preference-based baselines, particularly in high-dimensional problems with steep optima. The work represents a meaningful transfer of ideas from high-dimensional BO literature into the preferential setting, potentially broadening the practical applicability of human-feedback-driven optimization.
What's missing
The paper does not report results from user studies with actual human raters; all preference feedback in benchmarks appears to be simulated. Scalability limits (e.g., maximum dimensionality tested) and computational overhead of the derivative-informed approach relative to baselines are not detailed in the abstract. Open questions include how the methods perform under noisy or inconsistent human preferences and whether the Laplace approximation introduces meaningful error in practice.
What different sources said
- arXiv cs.LGCenter
Local Preferential Bayesian Optimization
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