A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization

Understand

Bayesian Optimization, the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years.

• In particular, much attention has been paid to improving its efficiency on problems with many parameters to optimize.
• This attention has trickled down to the workhorse of high dimensional BO, high dimensional Gaussian process regression, which is also of independent interest.
• The great flexibility that the Gaussian process prior implies is a boon when modeling complicated, low dimensional surfaces but simply says too little when dimension grows too large.