Continual Learning in Linear Classification on Separable Data

• URL: http://arxiv.org/abs/2306.03534v1
• Date: Tue, 6 Jun 2023 09:34:11 GMT
• Title: Continual Learning in Linear Classification on Separable Data
• Authors: Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
• Abstract summary: We show that learning with weak regularization reduces to solving a sequential max-margin problem. We then develop upper bounds on the forgetting and other quantities of interest under various settings. We discuss several practical implications to popular training practices like regularization scheduling and weighting.
• Score: 34.78569443156924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We analyze continual learning on a sequence of separable linear classification tasks with binary labels. We show theoretically that learning with weak regularization reduces to solving a sequential max-margin problem, corresponding to a special case of the Projection Onto Convex Sets (POCS) framework. We then develop upper bounds on the forgetting and other quantities of interest under various settings with recurring tasks, including cyclic and random orderings of tasks. We discuss several practical implications to popular training practices like regularization scheduling and weighting. We point out several theoretical differences between our continual classification setting and a recently studied continual regression setting.

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