Lifelong Open-Ended Probability Predictors

Omid Madani

Abstract:

We advance probabilistic multiclass prediction on open-ended streams
of items. In this setting, a predictor must emit items with
probabilities, and adapt to significant non-stationarity, including
new item appearances and frequency changes. The predictor is not given
the set of items that it is to predict a priori, and moreover the
totality of the items can grow unbounded: the space-limited predictor
need only track the currently salient items and their probabilities.
We develop Sparse Moving Average techniques (SMAs), including
adaptations of sparse EMA as well as novel queue-based methods with
dynamic per-item histories. For performance evaluation, to handle new
items, we develop a bounded version of log-loss. Our findings, on a
range of synthetic and real data streams, show that dynamic
predictand-specific (per connection) parameters, such as learning
rates, enhance both adaptation speed and stability.

Submission Type: Long submission (more than 12 pages of main content)

Changes Since Last Submission: Additional labels for the curves inside the plots, more citations, an extra experimental page (comparing to ADWIN) in the appendix (and corresponding code is provided as well), a few more results (eg further data points on referee window size choice), and some wording changes (eg some properties, on the Qs technique, are now summarized as technical lemmas).

Video: https://www.youtube.com/watch?v=bqbRYI4sLRA

Code: https://github.com/omadaniTet/sparse-moving-averages/

Assigned Action Editor: ~Martin_Mundt1

Submission Number: 6430