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Online temporally adaptive parameter estimation with applications to streaming data analysis.

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If you have a question about this talk, please contact Rachel Fogg .

Online learning algorithms deployed in streaming data contexts may be additionally required to possess temporally adaptive properties, in order to remain up-to-date against unforeseen changes, smooth or abrupt, in the underlying data generation mechanism. In cases where explicit dynamic modelling is either impossible or impractical, temporally adaptive behaviour may still be induced by controlling the responsiveness of the estimator to novel information. This can be naturally accomplished in certain algorithms that feature user-specified learning rates, such as the Robbins-Monro family of algorithms. We discuss available methodology for automatic self-tuning learning rates in a Robbins-Monro context. On the basis of both theoretical insights and real-data experiments, we demonstrate that this approach can efficiently handle temporal variation of unknown characteristics, while additionally serving as a monitoring tool.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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