Title: Learning to Weight Parameters for Data Attribution
Primary area: interpretability and explainable AI
Keywords: Training Data attribution; Influence function

Abstract: We study gradient-based data attribution, aiming to identify which training examples most influence a given output. Existing methods for this task either treat network parameters uniformly or rely on implicit weighting derived from Hessian approximations, which do not fully model functional heterogeneity of network parameters.
To address this, we propose a method to explicitly learn parameter importance weights directly from data, without requiring annotated labels.
Our approach improves attribution accuracy across diverse tasks, including image classification, language modeling, and diffusion, and enables fine-grained attribution for concepts like subject and style.

TL;DR: Attribution signals are noisy. Our method learns to re-weight layers to amplify the true signal, boosting accuracy and enabling fine-grained (e.g., subject vs. style) attribution.