Bio: Scott is a co-founder and CEO of SigOpt, providing optimization tools as a service, helping experts optimally tune their machine learning models. Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes' 30 under 30 in 2016.
Abstract: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize computational model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and our approach as well as give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.