Local Preferential Bayesian Optimization Methods for High-Dimensional Problems
Researchers have developed local preferential Bayesian optimization (PBO) methods that improve optimization efficiency by learning from pairwise human feedback rather than explicit objective functions. The new approach adapts trust-region and derivative-informed local search techniques to preference-based settings, addressing limitations of existing global search methods in high-dimensional problems. This advancement is significant for real-world applications like policy search where explicit objective functions are difficult to formulate but human preferences are readily available.
A new family of local preferential Bayesian optimization methods has been introduced to overcome the limitations of existing global search approaches in high-dimensional optimization problems. Traditional Bayesian optimization requires an explicit objective function, while preferential BO learns from pairwise human feedback—eliminating the need for explicit formulation. The proposed local PBO methods transfer key ideas from high-dimensional BO to the preferential setting, incorporating trust-region approaches and derivative-informed local search that exploit first- and second-order derivatives of the Laplace-approximated Gaussian process posterior. Benchmarking across GP sample paths, standard optimization functions, and policy-search tasks demonstrates that local PBO methods substantially reduce cumulative regret compared to global preference-based baselines, particularly in high-dimensional spaces and complex landscapes with steep optima. These methods show promise for practical applications where preference-based feedback is more natural than explicit objective specification.
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- arXiv cs.LGCenter
Local Preferential Bayesian Optimization
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