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Adaptive learning rates and Bayesian updating in the presence of drift, with applications

Thursday, July 11, 2013 - 11:00
Christoforos Anagnostopoulos, Department of Mathematics, Imperial College, UK
Room 4192, Earth Sciences Building (2207 Main Mall)

Streaming data has become ubiquitous as fast-paced automatic data collection is becoming routine in a variety of settings. Such settings require incremental model updates to handle continual data arrival, and adaptivity to handle temporal variation of possibly unknown characteristics. In this talk we will explore practical alternatives to Bayesian dynamic modelling that make use of forgetting factors and stochastic approximation techniques in order to maximise computational efficiency relying on only weak assumptions about the underlying dynamics. Particular emphasis will be paid to hybrid approaches.Applications of interest include streaming classification and multi-armed bandit problems.

Dr Anagnostopoulos is a Lecturer in Statistics in the Department of Mathematics in Imperial College London, prior to which he was a Research Fellow at the Statistical Laboratory in the University of Cambridge. His research interests are focused on statistical methods for streaming data analysis, including streaming regression, anomaly detection and network analysis. His theoretical interests involve stochastic approximation and state-space modelling. He has consulting experience in e-commerce, retail banking and online advertising, and is an advisor to a UK start-up on statistical software for streaming data analysis.