Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation

• URL: http://arxiv.org/abs/2301.10123v1
• Date: Tue, 24 Jan 2023 16:43:29 GMT
• Title: Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
• Authors: Henry B. Moss, Sebastian W. Ober and Victor Picheny
• Abstract summary: We show that existing methods for allocating inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy for use in BO.
• Score: 9.732863739456036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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