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

A toolkit for quantitative evaluation of data attribution methods.

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

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

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)

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

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

[ICML 2026] SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) 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.

Computation-level data lineage, gradient attribution, and provenance-guided unlearning in production ML.

Mechanistic Interpretability as a Control System: Dissociations, Data Attribution, and Autonomous Self-Repair in Language Models