Taking the Human out of the Loop - A Review of Bayesian Optimization (REVIEW)

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016). Shahriari et al. Proceedings of the IEEE

Introduction

  • “Mathematically we are considering the problem of finding a global maximizer (or minimizer) of an unknown objective function f, where X is some design space of interest; …”
  • “…in global optimization, X is often a compact subset of R^d but the Bayesian optimization framework can be applied to more unusual search spaces that involve categorical or conditional inputs.”
  • “The Bayesian posterior represents our updates beliefs - given data - on the likely objective function we are optimizing. Equipped with this probabilistic model, we can sequentially induce acquisition functions that leverage the uncertainty in the posterior to guide exploration.”
  • “Intuitively, the acquisition function evaluates the utility of candidate points for the next evaluation of f; therefore x_n+1 is selected by maximizing \alpha_n”