Title: MAGIC: Near-Optimal Data Attribution for Deep Learning
Primary area: interpretability and explainable AI
Keywords: data attribution, data valuation, influence function

Abstract: The goal of data attribution is to estimate how adding or removing
  a given set of training datapoints will affect model predictions.
  This goal is straightforward in convex settings, 
  but in non-convex settings, existing methods are far less
  successful. Current methods' estimates often only weakly
  correlate with the ground truth.
  In this work, we present a new data attribution
  method (MAGIC) that combines classical methods and recent advances in
  metadifferentiation (Engstrom et al., 2025) to nearly optimally
  estimate the effect of adding or removing training data on model predictions
  at the cost of only 2-3x the cost of training a single model.
  MAGIC essentially "solves" data attribution as it is currently
  studied, thus enabling downstream applications 
  and motivating more fine-grained future evaluations.

TL;DR: We present a method that nearly perfectly predicts the counterfactual effect of changing the training dataset on deep learning model.