Implicit Target Shift in Online Learning: Characterization and Correction
The work analyzes online kernel regression by showing it is mathematically equivalent to offline learning with a shifted target. It introduces a framework to characterize this shift and provides a method, target correction, to actively compensate for it. This correction allows online learners to provably recover the performance of their offline counterparts in continual learning settings.