Bayesian Network Meetup – Bayesian Crowd; Scalable Information Combining for Citizen Science & Crowdfunding
Date: 24 January 2016
Time: 18:30 – 21:00
We’re very pleased to announce our sixth meetup speaker, Stephen Roberts, Professor of Machine Learning at the University of Oxford.
In Citizen Science and Crowdsourcing applications, information from large numbers of agents need to be combined in an intelligent manner. For realistic deployment methods such combination should conform to optimality wherever possible, yet scale well with large numbers of information sources and amounts of data. This talk will focus on Bayesian information aggregation models; we discuss how the use of approximate inference, based on variational learning, allows excellent scaling properties whilst retaining high performance. We showcase the breadth of applicability of the approach with examples from large Citizen Science and Crowdsourcing domains, feedback and user-task allocation mechanisms.
18:30: Arrival and refreshments
20:00 – 20:15: Questions
20:15 – 21:00: Networking with food and drinks
Professor Stephen Roberts:
Stephen’s main area of research lies in machine learning approaches to data analysis. He has particular interests in the development of machine learning theory for problems in time series analysis and decision theory. Current research applies Bayesian statistics, graphical models and information theory to diverse problem domains including astronomy, mathematical biology, finance and sensor networks. He leads the Machine Learning Research Group, is a Professorial Fellow of Somerville College, Director of the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS) and Director of the Oxford-Man Institute.
Registration: please email email@example.com