GMED-editedexamplesremain similar to their unedited forms, but can yield increased loss in the upcoming model updates, thereby making thefuture replays more effectiveinovercoming catastrophic forgetting.

This paper introduces a new approach for the scalableTucker decomposition problem.

The meta model seeks to answer "what would the outcome be if we were to deploy a different

On the positive side, we give anα = 1.01-approximate proper learner that uses O(1/( 2γ2)) samples (which is optimal) and runs in timepoly(d/) 2 O(1/γ

Figure 2:Transfermatrix, see Section 4.2.