> For the complete documentation index, see [llms.txt](https://agi.university/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agi.university/forget-me-not-process.md).

# Forget-me-not-Process

Kieran Milan†, Joel Veness†, James Kirkpatrick, Demis Hassabis from DeepMind

Anna Koop, Michael Bowling from University of Alberta

We introduce the Forget-me-not Process, an efficient, non-parametric metaalgorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources. Our method works by taking a Bayesian approach to partitioning 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.