2026
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
ICLR 2026poster
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under training randomness: the same example may appear critical in one run and irrelevant in the next. Such instability undermi…