Accumulating Knowledge for Lifelong Online Learning

TL;DR

This paper introduces a new framework for lifelong online learning that enables continuous, interactive learning across tasks without prior data collection, supported by theoretical analysis and experimental validation.

Contribution

It proposes a computationally efficient lifelong online learning algorithm that combines current task predictions with accumulated knowledge, handling unknown task distributions and data sizes.

Findings

The algorithm achieves a small cumulative error under mild conditions.

Theoretical analysis provides an upper bound on cumulative error.

Experimental results validate the algorithm's effectiveness on synthetic and real data.

Abstract

Lifelong learning can be viewed as a continuous transfer learning procedure over consecutive tasks, where learning a given task depends on accumulated knowledge --- the so-called knowledge base. Most published work on lifelong learning makes a batch processing of each task, implying that a data collection step is required beforehand. We are proposing a new framework, lifelong online learning, in which the learning procedure for each task is interactive. This is done through a computationally efficient algorithm where the predicted result for a given task is made by combining two intermediate predictions: by using only the information from the current task and by relying on the accumulated knowledge. In this work, two challenges are tackled: making no assumption on the task generation distribution, and processing with a possibly unknown number of instances for each task. We are providing a theoretical analysis of this algorithm, with a cumulative error upper bound for each task. We find that under some mild conditions, the algorithm can still benefit from a small cumulative error even when facing few interactions. Moreover, we provide experimental results on both synthetic and real datasets that validate the correct behavior and practical usefulness of the proposed algorithm.