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The Forget-me-not Process

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We introduce the Forget-me-not Process, an efficient, non-parametric meta-algorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources. Our method works by taking a Bayesian approach to partition a stream of data into postulated task-specific segments, while simultaneously building a model for each task. We provide regret guarantees with respect to piecewise stationary data sources under the logarithmic loss, and validate the method empirically across a range of sequence prediction and task identification problems.

Citation

K. Milan, J. Veness, J. Kirkpatrick, M. Bowling, A. Koop, D. Hassabis. "The Forget-me-not Process". Neural Information Processing Systems, (ed: Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett), pp 3702-3710, December 2016.

Keywords:  
Category: In Conference
Web Links: NeurIPS

BibTeX

@incollection{Milan+al:NeurIPS16,
  author = {Kieran Milan and Joel Veness and James Kirkpatrick and Michael
    Bowling and Anna Koop and Demis Hassabis},
  title = {The Forget-me-not Process},
  Editor = {Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle
    Guyon, Roman Garnett},
  Pages = {3702-3710},
  booktitle = {Neural Information Processing Systems},
  year = 2016,
}

Last Updated: October 28, 2020
Submitted by Sabina P

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