用单调神经网络集成提升偏好探索的贝叶斯优化效果
Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble
- 用神经网络集成建模单调效用函数，支持成对比较数据
- 在多个真实任务中优于现有方法，噪声下仍表现稳健
- 适合多目标优化中需高效探索用户偏好的场景
许多现实世界的黑箱优化问题存在多个冲突目标。与尝试逼近全部帕累托最优解不同，交互式偏好学习可聚焦于最相关的子集。然而，此前研究较少利用效用函数通常具有单调性的特点。本文针对贝叶斯优化中的偏好探索（BOPE）问题，提出采用神经网络集成作为效用代理模型。该方法天然融合单调性约束，并支持成对比较数据。实验表明，所提方法优于当前最先进的技术，在效用评估存在噪声时仍具鲁棒性。消融实验证明单调性对性能提升至关重要。
原文摘要 · Abstract (English)
Many real-world black-box optimization problems have multiple conflicting objectives. Rather than attempting to approximate the entire set of Pareto-optimal solutions, interactive preference learning allows to focus the search on the most relevant subset. However, few previous studies have exploited the fact that utility functions are usually monotonic. In this paper, we address the Bayesian Optimization with Preference Exploration (BOPE) problem and propose using a neural network ensemble as a utility surrogate model. This approach naturally integrates monotonicity and supports pairwise comparison data. Our experiments demonstrate that the proposed method outperforms state-of-the-art approaches and exhibits robustness to noise in utility evaluations. An ablation study highlights the critical role of monotonicity in enhancing performance.
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