A toolkit for quantitative evaluation of data attribution methods.

Mapping out the "memory" of neural nets with data attribution

Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)

Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"

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A unified framework for attributing model components, data, and training dynamics to model behavior.

Official implementation of the paper "Most Influential Subset Selection: Challenges, Promises, and Beyond" (NeurIPS2024)

part of the MIT Center for Brains Minds + Machines computational tutorial series

[ICLR2025] An Efficient Framework for Crediting Data Contributors of Diffusion Models

[ICCV 2025] Code for training and testing AR-Detector method from the paper "Region-Level Data Attribution for Text-to-Image Generative Models"

Applied TRAK data attribution and Grad-CAM on a ResNet-18 model trained on the CIFAR-10 dataset to analyze which training samples most strongly influenced the model’s predictions and to examine the shared representations between influential samples and model outputs.