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Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier · Luigi Nardi · Matthias Poloczek

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #527

Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayesian optimization (HDBO) suffer from degrading performance as the number of dimensions increases, or even risk failure if certain unverifiable assumptions are not met. This paper proposes BAxUS that leverages a novel family of nested random subspaces to adapt the space it optimizes over to the problem. This ensures high performance while removing the risk of failure, which we assert via theoretical guarantees. A comprehensive evaluation demonstrates that BAxUS achieves better results than the state-of-the-art methods for a broad set of applications.

Author Information

Leonard Papenmeier (Lund University)
Luigi Nardi (Lund University and Stanford University)
Matthias Poloczek (Amazon)

Matthias’ research interests lie at the intersection of machine learning and optimization, with a focus on Bayesian methods for "exotic" optimization problems arising in business applications and in the natural sciences. He is a Principled Scientist at Amazon. Previously, Matthias was a Senior Manager at Uber AI, where he founded Uber’s Bayesian optimization team and led the cross-org effort that built a company-wide service to tune ML models at scale. Matthias received his PhD in CS from Goethe University in Frankfurt in 2013 and then worked as a postdoc at Cornell with David Williamson and Peter Frazier from 2014 until 2017. He was an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona from 2017 until 2019.

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