Keynote Talk
in
Workshop: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)

Keynote by Peter Frazier: Grey-box Bayesian Optimization for AutoML

Peter Frazier

2019 Keynote Talk
in
Workshop: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)

[ Video]

Abstract

Bayesian optimization is a powerful and flexible tool for AutoML. While BayesOpt was first deployed for AutoML simply as a black-box optimizer, recent approaches perform grey-box optimization: they leverage capabilities and problem structure specific to AutoML such as freezing and thawing training, early stopping, treating cross-validation error minimization as multi-task learning, and warm starting from previously tuned models. We provide an overview of this area and describe recent advances for optimizing sampling-based acquisition functions that make grey-box BayesOpt significantly more efficient.

Speaker

Peter Frazier

Peter Frazier is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University. He is also a Staff Data Scientist at Uber, where he managed the data science group for UberPOOL while on sabbatical leave from Cornell. He completed his Ph.D. in Operations Research and Financial Engineering at Princeton University in 2009. Peter's research is in Bayesian optimization, multi-armed bandits and incentive design for social learning, with applications in e-commerce, the sharing economy, and materials design. He is the recipient of an AFOSR Young Investigator Award and an NSF CAREER Award.

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