Modelling in the Context of Massively Missing Data (Talk)
- Neill Lawrence (Professor of Machine Learning)
- University of Sheffield, Department of Computer Science
In the age of large streaming data it seems appropriate to revisit the foundations of what we think of as data modelling. In this talk I'll argue that traditional statistical approaches based on parametric models and i.i.d. assumptions are inappropriate for the type of large scale machine learning we need to do in the age of massive streaming data sets. Particularly when we realise that regardless of the size of data we have, it pales in comparison to the data we could have. This is the domain of massively missing data. I'll be arguing for flexible non-parametric models as the answer. This presents a particular challenge, non parametric models require data storage of the entire data set, which presents problems for massive, streaming data. I will present a potential solution, but perhaps end with more questions than we started with.
Biography: 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 personalized health and computational biology, but happily dabbles in other areas such as speech, vision and graphics. Neil was Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (from 2011-2013) and is an Action Editor for the Journal of Machine Learning Research. He was the founding editor of the JMLR Workshop and Conference Proceedings (2006) and is currently series editor. He was an area chair for the NIPS conference in 2005, 2006, 2012 and 2013, Workshops Chair in 2010 and Tutorials Chair in 2013. He was general chair of AISTATS in 2010 and AISTATS Programme Chair in 2012. He was Program Chair of NIPS in 2014 and is General Chair for NIPS 2015.
Details
- 18 March 2015 • 11:00 am - 12:00 am
- MPH Lecture Hall, Tübingen
- Intelligent Systems
