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Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge and Chief Scientist at Uber.
My current research interests include Bayesian approaches to machine learning, artificial intelligence, statistics, information retrieval, bioinformatics, and computational motor control. Statistics provides the mathematical foundations for handling uncertainty, making decisions, and designing learning systems. I have recently worked on Gaussian processes, non-parametric Bayesian methods ...
An approach to semi-supervised learning is pro-posed that is based on a Gaussian random field model. Labeled and unlabeled data are rep-resented as vertices in a weighted graph, with edge weights encoding the similarity between in-stances. The learning problem is then formulated in terms of a Gaussian random field on this graph, where the mean of the field is characterized in terms of harmonic ...
Zoubin Ghahramani is a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian nonparametric approaches to machine learning systems, and to the development of approximate ...
Jun 6, 2015 · In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs -- extracting information from existing models that has been thrown away so far.
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Zoubin Ghahramani FRS is Professor of Information Engineering at the University of Cambridge, a Fellow of St John's College, former Chief Scientist at Uber, and currently a Distinguished Researcher at Google AI.
