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Bayesian Optimization with a Neural Network Meta-learned on Synthetic Data Only
Samuel Müller · Sebastian Pineda Arango · Matthias Feurer · Josif Grabocka · Frank Hutter
Event URL: https://openreview.net/forum?id=9xCudkMSkC »

Bayesian Optimization (BO) is an effective approach to optimize black-box functions, relying on a probabilistic surrogate to model the response surface. In this work, we propose to use a Prior-data Fitted Network (PFN) as a cheap and flexible surrogate. PFNs are neural networks that approximate the Posterior Predictive Distribution (PPD) in a single forward-pass. Most importantly, they can approximate the PPD for any prior distribution that we can sample from efficiently. Additionally, we show what is required for PFNs to be used in a standard BO setting with common acquisition functions. We evaluated the performance of a PFN surrogate for Hyperparameter optimization (HPO), a major application of BO. While the method can still fail for some search spaces, we fare comparable or better than the state-of-the-art on the HPO-B and PD1 benchmark.

Author Information

Samuel Müller (University of Freiburg)
Sebastian Pineda Arango (Albert-Ludwigs-Universität Freiburg)
Matthias Feurer (Universität Freiburg)
Josif Grabocka (Universität Freiburg)
Frank Hutter (University of Freiburg & Bosch)

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he previously was an assistant professor 2013-2017. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

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