论文标题
顶级$ k $排名贝叶斯优化
Top-$k$ Ranking Bayesian Optimization
论文作者
论文摘要
本文介绍了一种新颖的方法，用于顶级$ k $排名贝叶斯优化（$ K $排名BO），这是对优先BO的实用而重要的概括，以处理顶级$ K $排名和TIE/TIE/冷漠观察。我们首先设计了一个替代模型，该模型不仅能够迎合上述观察结果，而且还得到了经典的随机效用模型的支持。另一个同样重要的贡献是在BO中引入了首个信息理论获取函数，其优先观察称为多项式预测熵搜索（MPE），该搜索（MPES）在处理这些观察结果方面具有灵活性，并针对共同查询的所有输入进行了优化。与现有的采集功能相比，MPE具有较高的性能，这些功能一次贪婪地选择了查询的输入。我们使用多个合成基准功能，CIFAR- $ 10 $数据集和寿司偏好数据集从经验上评估MPE的性能。
This paper presents a novel approach to top-$k$ ranking Bayesian optimization (top-$k$ ranking BO) which is a practical and significant generalization of preferential BO to handle top-$k$ ranking and tie/indifference observations. We first design a surrogate model that is not only capable of catering to the above observations, but is also supported by a classic random utility model. Another equally important contribution is the introduction of the first information-theoretic acquisition function in BO with preferential observation called multinomial predictive entropy search (MPES) which is flexible in handling these observations and optimized for all inputs of a query jointly. MPES possesses superior performance compared with existing acquisition functions that select the inputs of a query one at a time greedily. We empirically evaluate the performance of MPES using several synthetic benchmark functions, CIFAR-$10$ dataset, and SUSHI preference dataset.
