Treatment Effect Estimation to Guide Model Optimization in Continual Learning

Jonas Seng, Florian Peter Busch, Matej Zecevic, Moritz Willig

Abstract: Continual Learning systems are faced with a potentially large numbers of tasks to be learned while the models employed have only limited capacity available, which makes it potentially impossible to learn all required tasks within a single model. In order to detect on when a model might break we propose to use treatment effect estimation techniques to estimate the effect of training a model on a new task w.r.t. some suitable performance measure.