Continual Learning in Linear Classification on Separable Data
Itay Evron,u00a0Edward Moroshko,u00a0Gon Buzaglo,u00a0Maroun Khriesh,u00a0Badea Marjieh,u00a0Nathan Srebro,u00a0Daniel Soudry
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.
