Personalized Online Machine Learning

TL;DR

This paper introduces POSL, an online ensembling algorithm that personalizes predictions for streaming data by adapting to individual or group characteristics, demonstrating improved performance in real-time forecasting scenarios.

Contribution

The paper presents a novel online ensembling method, POSL, capable of personalized learning across diverse data structures and dynamically adjusting to changing environments.

Findings

POSL outperforms existing methods in realistic simulations.

POSL provides reliable, adaptive predictions for time-series data.

Extension to dynamic entry/exit of time-series enhances practicality.

Abstract

In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization. Namely, POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized (i.e., optimization with respect to baseline covariate subject ID) to many individuals (i.e., optimization with respect to common baseline covariates). As an online algorithm, POSL learns in real-time. POSL can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed algorithms that are never updated during the procedure, pooled algorithms that learn from many individuals' time-series, and individualized algorithms that learn from within a single time-series. POSL's ensembling of this hybrid of base learning strategies depends on the amount of data collected, the stationarity of the time-series, and the mutual characteristics of a group of time-series. In essence, POSL decides whether to learn across samples, through time, or both, based on the underlying (unknown) structure in the data. For a wide range of simulations that reflect realistic forecasting scenarios, and in a medical data application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for time-series data and adjust to changing data-generating environments. We further cultivate POSL's practicality by extending it to settings where time-series enter/exit dynamically over chronological time.