Deeper Understanding of Black-box Predictions via Generalized Influence Functions

arXiv:2312.05586v2 Announce Type: replace
Abstract: Influence functions (IFs) elucidate how training data changes model behavior. However, the increasing size and non-convexity in large-scale models make IFs inaccurate. We suspect that the fragility comes from the first-order approximation which may cause nuisance changes in parameters irrelevant to the examined data. However, simply computing influence from the chosen parameters can be misleading, as it fails to nullify the hidden effects of unselected parameters on the analyzed data. Thus, our approach introduces generalized …

arxiv box cs.ai cs.lg functions generalized influence predictions type understanding via