Improving Influence Functions for Accurate Modeling of Black-box Predictions by Focusing on Relevant Parameters

The core message of this paper is that influence functions can be improved by focusing on the parameters that are most relevant to the input data, rather than updating all parameters. This can lead to more accurate and robust model updates when removing or changing training data.