Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy designed specifically for use in BO. Unlike existing methods which seek only to reduce global uncertainty in the objective function, our approach provides the local high-fidelity modelling of promising regions required for precise optimisation. More generally, we demonstrate that our proposed framework provides a flexible way to allocate modelling capacity in sparse models and so is suitable broad range of downstream sequential decision making tasks.

翻译：稀疏高斯过程是高通量贝叶斯优化（BO）循环的关键组成部分；然而，我们证明现有诱导点分配方法严重制约了优化性能。通过利用行列式点过程的质量-多样性分解，我们提出了首个专为BO设计的诱导点分配策略。与仅旨在降低目标函数全局不确定性的现有方法不同，本方法能够对具有优化潜力的区域进行局部高保真建模，从而实现精确优化。更广泛而言，我们证明所提出的框架为稀疏模型中的建模能力分配提供了灵活途径，因此适用于广泛的序列决策任务。