Efficient computation of the Knowledge Gradient for Bayesian Optimization
This work addresses a computational bottleneck in Bayesian optimization for researchers and practitioners, though it is incremental as it builds on existing approximations.
The paper tackles the problem of efficiently computing the Knowledge Gradient acquisition function in Bayesian optimization, which lacks an analytical solution, by proposing One-shot Hybrid KG, a new method that combines previous ideas to reduce computational overhead while maintaining or improving performance, as shown empirically.
Bayesian optimization is a powerful collection of methods for optimizing stochastic expensive black box functions. One key component of a Bayesian optimization algorithm is the acquisition function that determines which solution should be evaluated in every iteration. A popular and very effective choice is the Knowledge Gradient acquisition function, however there is no analytical way to compute it. Several different implementations make different approximations. In this paper, we review and compare the spectrum of Knowledge Gradient implementations and propose One-shot Hybrid KG, a new approach that combines several of the previously proposed ideas and is cheap to compute as well as powerful and efficient. We prove the new method preserves theoretical properties of previous methods and empirically show the drastically reduced computational overhead with equal or improved performance. All experiments are implemented in BOTorch and code is available on github.