论文标题
有效的上下文优惠贝叶斯优化与历史示例
Efficient Contextual Preferential Bayesian Optimization with Historical Examples
论文作者
论文摘要
最先进的多目标优化通常假定已知的效用功能，交互式学习或计算完整的帕累托前台，需要昂贵的专家输入。为了减少专家的参与，我们提出了一种脱机，可解释的公用事业学习方法，该方法使用专家知识，历史示例和有关公用事业空间的粗略信息来减少样本要求。我们通过完整的贝叶斯后部对不确定性进行建模，并在整个优化过程中传播它。我们的方法的表现优于四个领域的标准高斯流程和BOPE，即使在现实世界中遇到的有偏见的样本和有限的专家输入，也表现出强烈的性能。
State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.~Real-world problems, however, involve implicit preferences that are hard to formalize. To reduce expert involvement, we propose an offline, interpretable utility learning method that uses expert knowledge, historical examples, and coarse information about the utility space to reduce sample requirements. We model uncertainty via a full Bayesian posterior and propagate it throughout the optimization process. Our method outperforms standard Gaussian processes and BOPE across four domains, showing strong performance even with biased samples, as encountered in the real-world, and limited expert input.
