Toward Embedding Bayesian Optimization in the Lab: Reasoning about Resource and Actions

A key contribution of this paper is to introduce an extended BO setting, called Bayesian Optimization with Resources We consider optimizing an unknown function f by running (BOR), that explicitly models experimental resources experiments that each take an input x and return a noisy output and activities. In particular, our model specifies f(x). In particular, we focus on the setting where experiments the following: 1) resource requirements for experiments, are expensive, limiting the number of experiments which may vary across different experiments, 2) resourceproduction that can be run. Bayesian Optimization (BO) addresses this actions, which produce the various resources and setting by maintaining a Bayesian posterior over f to capture can require varying amounts of time, and 3) a set of "labs" our uncertainty about f given prior experiments (Jones for running concurrent experiments and a set of "production 2001; Brochu, Cora, and de Freitas 2010). The posterior is lines" for concurrent resource production. The problem is then used to select new experiments that trades-off exploring then to select and schedule the experiments and resourceproduction uncertain areas of the experimental space and exploiting actions in order to optimize the unknown objective promising areas.