Bayesian optimization has emerged as an exciting subfield of machine learning that is concerned with the global optimization of noisy, black-box functions using probabilistic methods. Systems implementing Bayesian optimization techniques have been successfully used to solve difficult problems in a diverse set of applications. There have been many recent advances in the methodologies and theory underpinning Bayesian optimization that have extended the framework to new applications as well as provided greater insights into the behaviour of these algorithms. Bayesian optimization is now increasingly being used in industrial settings, providing new and interesting challenges that require new algorithms and theoretical insights.
At last year’s NIPS workshop on Bayesian optimization the focus was on the intersection of “Theory and Practice”. The workshop this year will follow this trend by again looking at theoretical contributions, but also by focusing on the practical side of Bayesian optimization in industry. The goal of this workshop is not only to bring together both practical and theoretical research knowledge from academia, but also to facilitate cross-fertilization with industry. Specifically, we would like to carefully examine the types of problems where Bayesian optimization works well in industrial settings, but also the types of situations where additional performance is needed. The key questions we will discuss are: how to scale Bayesian optimization to long time-horizons and many observations? How to tackle high-dimensional data? How to make Bayesian optimization work in massive, distributed systems? What kind of structural assumptions are we able to make? And finally, what can we say about these questions both empirically and theoretically?
The target audience for this workshop consists of both industrial and academic practitioners of Bayesian optimization as well as researchers working on theoretical advances in probabilistic global optimization. To this end we have invited many industrial users of Bayesian optimization to attend and speak at the workshop. We expect this exchange of industrial and academic knowledge will lead to a significant interchange of ideas and a clearer understanding of the challenges and successes of Bayesian optimization as a whole.
A further goal is to encourage collaboration between the diverse set of researchers involved in Bayesian optimization. This includes not only interchange between industrial and academic researchers, but also between the many different sub-fields of machine learning which make use of Bayesian optimization. We are also reaching out to the wider global optimization and Bayesian inference communities for involvement.
We would like to thank our program committee for their great help in reviewing submissions:
Shipra Agrawal, Javad Azimi, Remi Bardenet, Sebastien Bubeck, Roberto Calandra, Marc Deisenroth, Misha Denil, David Duvenaud, Roman Garnett, Michael Gelbart, Roger Grosse, Katherine Heller, Phillip Hennig, Frank Hutter, Emilie Kaufmann, Nathan Korda, Hugo Larochelle, Lihong Li, Ruben Martinez-Cantin, Michael Osborne, Ingmar Posner, Carl Rasmussen, Dan Russo, Bobak Shahriari, Ziyu Wang, and Kilian Weinberger.
Below are the papers accepted for the 2014 workshop. For papers accepted at previous workshops look here.