Yee Whye Teh: On Bayesian Deep Learning and Deep Bayesian Learning (NIPS 2017 Keynote)

Breiman Lecture by Yee Whye Teh on Bayesian Deep Learning and Deep Bayesian Learning.

Abstract:
Probabilistic and Bayesian reasoning is one of the principle theoretical pillars to our understanding of machine learning. Over the last two decades, it has inspired a whole range of successful machine learning methods and influenced the thinking of many researchers in the community. On the other hand, in the last few years the rise of deep learning has completely transformed the field and led to a string of phenomenal, era-defining, successes. In this talk I will explore the interface between these two perspectives on machine learning, and through a number of projects I have been involved in, explore questions like: How can probabilistic thinking help us understand deep learning methods or lead us to interesting new methods? Conversely, how can deep learning technologies help us develop advanced probabilistic methods?

Bio:
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.