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Bayesian Deep Learning
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling

Tue Dec 14 01:00 AM -- 11:00 AM (PST) @ None
Event URL: http://bayesiandeeplearning.org/ »

To deploy deep learning in the wild responsibly, we must know when models are making unsubstantiated guesses. The field of Bayesian Deep Learning (BDL) has been a focal point in the ML community for the development of such tools. Big strides have been made in BDL in recent years, with the field making an impact outside of the ML community, in fields including astronomy, medical imaging, physical sciences, and many others. But the field of BDL itself is facing an evaluation crisis: most BDL papers evaluate uncertainty estimation quality of new methods on MNIST and CIFAR alone, ignoring needs of real world applications which use BDL. Therefore, apart from discussing latest advances in BDL methodologies, a particular focus of this year’s programme is on the reliability of BDL techniques in downstream tasks. This focus is reflected through invited talks from practitioners in other fields and by working together with the two NeurIPS challenges in BDL — the Approximate Inference in Bayesian Deep Learning Challenge and the Shifts Challenge on Robustness and Uncertainty under Real-World Distributional Shift — advertising work done in applications including autonomous driving, medical, space, and more. We hope that the mainstream BDL community will adopt real world benchmarks based on such applications, pushing the field forward beyond MNIST and CIFAR evaluations.

Tue 1:00 a.m. - 11:00 a.m.
BDL Workshop times (Talks and poster sessions)

Author Information

Yarin Gal (University of Oxford)
Yingzhen Li (Imperial College London)

Yingzhen Li is a senior researcher at Microsoft Research Cambridge. She received her PhD from the University of Cambridge, and previously she has interned at Disney Research. She is passionate about building reliable machine learning systems, and her approach combines both Bayesian statistics and deep learning. Her contributions to the approximate inference field include: (1) algorithmic advances, such as variational inference with different divergences, combining variational inference with MCMC and approximate inference with implicit distributions; (2) applications of approximate inference, such as uncertainty estimation in Bayesian neural networks and algorithms to train deep generative models. She has served as area chairs at NeurIPS/ICML/ICLR/AISTATS on related research topics, and she is a co-organizer of the AABI2020 symposium, a flagship event of approximate inference.

Sebastian Farquhar (University of Oxford)
Christos Louizos (Qualcomm AI Research)
Eric Nalisnick (University of Amsterdam)
Andrew Gordon Wilson (New York University)
Zoubin Ghahramani (Uber and University of Cambridge)

Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, where he leads the Machine Learning Group. He studied computer science and cognitive science at the University of Pennsylvania, obtained his PhD from MIT in 1995, and was a postdoctoral fellow at the University of Toronto. His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His current research interests include statistical machine learning, Bayesian nonparametrics, scalable inference, probabilistic programming, and building an automatic statistician. He has held a number of leadership roles as programme and general chair of the leading international conferences in machine learning including: AISTATS (2005), ICML (2007, 2011), and NIPS (2013, 2014). In 2015 he was elected a Fellow of the Royal Society.

Kevin Murphy (Google)
Max Welling (University of Amsterdam / Qualcomm AI Research)

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