title: Kronfluence: Influence Functions with Eigenvalue-corrected Kronecker-Factored Approximate Curvature
abstract: When trying to gain better visibility into a machine learning model in order to understand and mitigate the associated risks, a potentially valuable source of evidence is: which training examples most contribute to a given behavior? Influence functions aim to answer a counterfactual: how would the model's parameters (and hence its outputs) change if a given sequence were added to the training set? While influence functions have produced insights for small models, they are difficult to scale to l
journal: arXiv (Cornell University)