Bayesian Optimization over Hybrid Spaces

Aryan Deshwal · Syrine Belakaria · Jana Doppa

Keywords: [ Algorithms ] [ Algorithms ] [ Adversarial Networks ] [ Classification ]

[ Abstract ]
[ Slides
[ Paper ]
[ Visit Poster at Spot B5 in Virtual World ]
Tue 20 Jul 9 p.m. PDT — 11 p.m. PDT
Spotlight presentation: Optimization 4
Tue 20 Jul 7 p.m. PDT — 8 p.m. PDT


We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in many real-world applications. For example, in materials design optimization via lab experiments, discrete and continuous variables correspond to the presence/absence of primitive elements and their relative concentrations respectively. The key challenge is to accurately model the complex interactions between discrete and continuous variables. In this paper, we propose a novel approach referred as Hybrid Bayesian Optimization (HyBO) by utilizing diffusion kernels, which are naturally defined over continuous and discrete variables. We develop a principled approach for constructing diffusion kernels over hybrid spaces by utilizing the additive kernel formulation, which allows additive interactions of all orders in a tractable manner. We theoretically analyze the modeling strength of additive hybrid kernels and prove that it has the universal approximation property. Our experiments on synthetic and six diverse real-world benchmarks show that HyBO significantly outperforms the state-of-the-art methods.

Chat is not available.