The Forget-Me-Not Process
Abstract
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.
Cite
Text
Milan et al. "The Forget-Me-Not Process." Neural Information Processing Systems, 2016.Markdown
[Milan et al. "The Forget-Me-Not Process." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/milan2016neurips-forgetmenot/)BibTeX
@inproceedings{milan2016neurips-forgetmenot,
title = {{The Forget-Me-Not Process}},
author = {Milan, Kieran and Veness, Joel and Kirkpatrick, James and Bowling, Michael and Koop, Anna and Hassabis, Demis},
booktitle = {Neural Information Processing Systems},
year = {2016},
pages = {3702-3710},
url = {https://mlanthology.org/neurips/2016/milan2016neurips-forgetmenot/}
}