Better Training Data Attribution via Better Inverse Hessian-Vector Products
accuracy improvementapproximation techniquescomputational efficiencyekfac-preconditionergradient-based methodsihvp approximationinfluence functionsinverse hessian-vector productiteration efficiencymodel behaviorneumann series iterationstda performancetraining data attributiontuningunrolled differentiation
Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and unrolled differentiation both involve a computation that resembles an inverse Hessian-vector product (iHVP), which is difficult to approximate efficiently. We introduce an algorithm (ASTRA) which uses the EKFAC-preconditioner on Neumann series iterations to arrive at an accurate iHVP approximation for TDA. ASTRA is easy to tune, requires fewer iterations than Neumann series iterations, and is more accurate than EKFAC-based approximations. Using ASTRA, we show that improving the accuracy of the iHVP approximation can significantly improve TDA performance.