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:斯普尔斯·高斯进程是高通量贝耶斯优化循环的一个关键组成部分;然而,我们表明,目前分配其诱导点的方法严重妨碍了最佳化表现。我们通过利用Dizminantal点进程的质量多样性分解,提出了专门为BBO设计的第一批引导点分配战略。与目前只力求减少客观功能中全球不确定性的方法不同,我们的方法为精确优化所需的有希望的区域提供了当地高忠诚度建模。更一般地说,我们提出的框架为分散模型分配建模能力提供了灵活的方式,因此是适当的一系列下游连续决策任务。