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Neil Lawrence

 

Thursday 23rd October 2014

Time: 4pm

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

“Approximate Inference in Deep GPs”

 

In this talk we will review deep Gaussian process models and relate them to neural network models. We will then consider the details of how variational inference may be performed in these models. The approach is centred on "variational compression", an approach to variational inference that compresses information into an augmented variable space.

The aim of the deep Gaussian process framework is to enable probabilistic learning of multi-modal data. We will therefore end by highlighting directions for future research and discussing application of these models in domains such as personalised health.

Bio

Neil Lawrence received his bachelor's degree in Mechanical Engineering from the University of Southampton in 1994. Following a period as an field engineer on oil rigs in the North Sea he returned to academia to complete his PhD in 2000 at the Computer Lab in Cambridge University. He spent a year at Microsoft Research in Cambridge before leaving to take up a Lectureship at the University of Sheffield, where he was subsequently appointed Senior Lecturer in 2005. In January 2007 he took up a post as a Senior Research Fellow at the School of Computer Science in the University of Manchester where he worked in the Machine Learning and Optimisation research group. In August 2010 he returned to Sheffield to take up a collaborative Chair in Neuroscience and Computer Science. Neil's main research interest is machine learning through probabilistic models. He focuses on both the algorithmic side of these models and their application. He has a particular focus on applications in computational biology, but happily dabbles in other areas such as speech, vision and graphics. His main publication area from a methodological perspective is Gaussian processes. He is know for two particular formalisms based on Gaussian process models: Latent Force Models and Gaussian Process Latent Variable Models. Neil is an Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence and an Action Editor for the Journal of Machine Learning Research. He was the founding editor of the JMLR Workshop and Conference Proceedings and is currently series editor. He is Program Chair for AISTATS 2012 and has served on the program committee of several international conferences and was an area chair for the NIPS conference in 2005 and 2006. He was general chair of AISTATS in 2010 (bringing the conference to Europe for the first time) and NIPS Workshop Chair, also in 2010; and program chair of NIPS in 2014.