Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search considers the entropy over the optimal value in the output space. We propose Joint Entropy Search (JES), a novel information-theoretic acquisition function that considers an entirely new quantity, namely the entropy over the joint optimal probability density over both input and output space. To incorporate this information, we consider the reduction in entropy from conditioning on fantasized optimal input/output pairs. The resulting approach primarily relies on standard GP machinery and removes complex approximations typically associated with information-theoretic methods. With minimal computational overhead, JES shows superior decision-making, and yields state-of-the-art performance for information-theoretic approaches across a wide suite of tasks. As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.

翻译：信息理论贝叶斯优化技术因其非显微性特性而为优化昂贵到评估黑盒功能而变得十分流行。 Entropy 搜索和预测 EntropySearch 都考虑在输入空间的最佳值之上的酶, 而最近的Max- valu EntropySearch 则考虑在输出空间的最佳值之上的酶。 我们提议了一种新颖的信息理论获取功能,即超过输入空间和输出空间的共同最佳概率密度的酶。 为了纳入这一信息,我们考虑减少对扇形最佳输入/输出配对的调节。 由此产生的方法主要依靠标准的GP机械, 并去除通常与信息理论方法相关的复杂近似值。 在最小的计算管理下, JES 显示高级决策, 并产生全套任务的信息理论方法的状态性能。 作为较强的结果, JESS 提供一种较轻的获取功能。