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Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks
Steven Adriaensen · Herilalaina Rakotoarison · Samuel Müller · Frank Hutter
Event URL: https://openreview.net/forum?id=VQpqxucNX63 »

Learning curve extrapolation aims to predict model performance in later epochs of a machine learning training, based on the performance in the first k epochs. In this work, we argue that, while the varying difficulty of extrapolating learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive. We describe the first application of prior-data fitted neural networks (PFNs) in this context. PFNs use a transformer, pre-trained on data generated from a prior, to perform approximate Bayesian inference in a single forward pass. We present preliminary results, demonstrating that PFNs can more accurately approximate the posterior predictive distribution multiple orders of magnitude faster than MCMC, as well as obtain a lower average error predicting final accuracy obtained by real learning curve data from LCBench.

Author Information

Steven Adriaensen (Universität Freiburg)
Herilalaina Rakotoarison (University of Freiburg)
Samuel Müller (University of 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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