# Local Preferential Bayesian Optimization

arXiv: https://arxiv.org/abs/2606.02351 · HTML: https://mlsanity.com/paper/2606.02351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 30 of 30)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Scalable Constrained Bayesian Optimization](https://mlsanity.com/paper/2002.08526/) (2020)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces](https://mlsanity.com/paper/1902.03229/) (2019)
- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [Local policy search with Bayesian optimization](https://mlsanity.com/paper/2106.11899/) (2021)
- [Local Bayesian optimization via maximizing probability of descent](https://mlsanity.com/paper/2210.11662/) (2022)
- [qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization](https://mlsanity.com/paper/2303.15746/) (2023)
- [Projective Preferential Bayesian Optimization](https://mlsanity.com/paper/2002.03113/) (2020)
- [Top-k Ranking Bayesian Optimization](https://mlsanity.com/paper/2012.10688/) (2020)
- [The Behavior and Convergence of Local Bayesian Optimization](https://mlsanity.com/paper/2305.15572/) (2023)
- [Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes](https://mlsanity.com/paper/2302.01513/) (2023)
- [Preferential Bayesian optimisation with Skew Gaussian Processes](https://mlsanity.com/paper/2008.06677/) (2020)
- [Principled Preferential Bayesian Optimization](https://mlsanity.com/paper/2402.05367/) (2024)
- [Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study](https://mlsanity.com/paper/1812.06325/) (2018)
- [Improving sample efficiency of high dimensional Bayesian optimization with MCMC](https://mlsanity.com/paper/2401.02650/) (2024)
- [Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization](https://mlsanity.com/paper/2405.15285/) (2024)
- [Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits](https://mlsanity.com/paper/2003.06495/) (2020)
- [User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2502.06971/) (2025)
- [NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information](https://mlsanity.com/paper/2510.05516/) (2025)
- [Sequential Preference-Based Optimization](https://mlsanity.com/paper/1801.02788/) (2018)
- [BayeSQP: Bayesian Optimization through Sequential Quadratic Programming](https://mlsanity.com/paper/2602.03232/) (2026)
- [Event-Triggered Safe Bayesian Optimization on Quadcopters](https://mlsanity.com/paper/2312.08058/) (2023)
- [CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2405.07760/) (2024)
- [Simulation-Aided Policy Tuning for Black-Box Robot Learning](https://mlsanity.com/paper/2411.14246/) (2024)
- [POLAR: Preference Optimization and Learning Algorithms for Robotics](https://mlsanity.com/paper/2208.04404/) (2022)
- [Local Bayesian Optimization for Controller Tuning with Crash Constraints](https://mlsanity.com/paper/2411.16267/) (2024)
- [Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization](https://mlsanity.com/paper/2512.14350/) (2025)
- [Lipschitz Safe Bayesian Optimization for Automotive Control](https://mlsanity.com/paper/2501.12969/) (2025)
- [Preferential Bayesian Optimization with Crash Feedback](https://mlsanity.com/paper/2604.01776/) (2026)

## Abstract (arXiv)

Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function. Preferential BO (PBO) removes this requirement by learning from pairwise human feedback, yet existing methods struggle to efficiently optimize beyond low- and medium-dimensional problems due to their global search approaches. We address this limitation by developing a family of local PBO methods that transfer key ideas from high-dimensional BO to the preferential setting. In particular, we introduce local PBO methods which adapt trust-region and derivative-informed local search to pairwise preference feedback, where the latter exploits first- and second-order derivatives of the Laplace-approximated GP posterior. Our benchmark on GP sample paths, standard optimization benchmark functions, and policy-search tasks shows that local PBO methods are especially effective in high-dimensional and complex landscapes with steep optima. Compared with global preference-based baselines, they can substantially reduce cumulative regret, making them particularly useful for real-world preference-based optimization tasks such as policy search.