Modulating Surrogates for Bayesian Optimization
This addresses a practical issue for users of Bayesian optimization in real-world applications, but it is incremental as it builds on existing surrogate modeling approaches.
The paper tackles the problem of Bayesian optimization (BO) failing on real-world objectives with challenging structures by proposing surrogate models that focus on well-behaved, informative patterns while ignoring detrimental details. The result shows improved reliability and performance on benchmarks, though no concrete numbers are provided.
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible, often fail to make progress by spending too many evaluations modeling irrelevant details. We address this issue by proposing surrogate models that focus on the well-behaved structure in the objective function, which is informative for search, while ignoring detrimental structure that is challenging to model from few observations. First, we demonstrate that surrogate models with appropriate noise distributions can absorb challenging structures in the objective function by treating them as irreducible uncertainty. Secondly, we show that a latent Gaussian process is an excellent surrogate for this purpose, comparing with Gaussian processes with standard noise distributions. We perform numerous experiments on a range of BO benchmarks and find that our approach improves reliability and performance when faced with challenging objective functions.