In this work, we show for the first time that error compensated gradient compression methods can be accelerated. In particular, we propose and study the error compensated loopless Katyusha method, and establish an accelerated linear convergence rate under standard assumptions.

Influence functions (IF) have been seen as a technique for explaining model predictions through the lens of the training data.