There have been many recent advances in the development of machine learning approaches for active decision making and optimization. These advances have occurred in seemingly disparate communities, each referring to the problem using different terminology: Bayesian optimization, experimental design, bandits, active sensing, automatic algorithm configuration, personalized recommender systems, etc. Recently, significant progress has been made in improving the methodologies used to solve high-dimensional problems and applying these techniques to challenging optimization tasks with limited and noisy feedback. This progress is particularly apparent in areas that seek to automate machine learning algorithms and website analytics. Applying these approaches to increasingly harder problems has also revealed new challenges and opened up many interesting research directions both in developing theory and in practical application.

Following on last year's NIPS workshop, "Bayesian Optimization & Decision Making", the goal of this workshop is to bring together researchers and practitioners from these diverse subject areas to facilitate cross-fertilization by discussing challenges, findings, and sharing data. This year we plan to focus on the intersection of "Theory and Practice". Specifically, we would like to carefully examine the types of problems where Bayesian optimization performs well and ask what theoretical guarantees can be made to explain this performance? Where is the theory lacking? What are the most pressing challenges? In what way can this empirical performance be used to guide the development of new theory?