Title: Bayesian Influence Functions for Hessian-Free Data Attribution
Primary area: interpretability and explainable AI
Keywords: Training Data Attribution, Interpretability, SGMCMC, Influence Functions, MCMC, Loss Landscape, Geometry, Singular Learning Theory, Robustness

Abstract: Classical influence functions face significant challenges when applied to deep neural networks, primarily due to non-invertible Hessians and high-dimensional parameter spaces. We propose the local Bayesian influence function (BIF), an extension of classical influence functions that replaces Hessian inversion with loss landscape statistics that can be estimated via stochastic-gradient MCMC sampling. This Hessian-free approach captures higher-order interactions among parameters and scales efficiently to neural networks with billions of parameters. We demonstrate state-of-the-art results on predicting retraining experiments.

TL;DR: We introduce a "Bayesian" generalization of influence functions that scales to models with billions of parameters.