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Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner · Zonghao Chen · Yee Whye Teh · Yarin Gal

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #509

Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters, infer an approximate posterior distribution, and use it to make stochastic predictions. However, explicit inference over neural network parameters makes it difficult to incorporate meaningful prior information about the data-generating process into the model. In this paper, we pursue an alternative approach. Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates. We show that the proposed method leads to state-of-the-art uncertainty estimation and predictive performance on a range of prediction tasks and demonstrate that it performs well on a challenging safety-critical medical diagnosis task in which reliable uncertainty estimation is essential.

Author Information

Tim G. J. Rudner (University of Oxford)

Tim G. J. Rudner is a Computer Science PhD student at the University of Oxford supervised by Yarin Gal and Yee Whye Teh. His research interests span Bayesian deep learning, reinforcement learning, and variational inference. He obtained a master’s degree in statistics from the University of Oxford and an undergraduate degree in mathematics and economics from Yale University. Tim is also a Rhodes Scholar and a Fellow of the German National Academic Foundation.

Zonghao Chen (Tsinghua University, Tsinghua University)
Yee Whye Teh (University of Oxford, DeepMind)

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.

Yarin Gal (University of OXford)

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