Data Leverage References

Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu

Comprehensive survey of machine unlearning covering definitions, scenarios, verification methods, and applications. Cited in the International AI Safety Report 2025 as a pioneering paradigm for removing sensitive information.

Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, Stella Biderman

Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, Aleksander Madry

Proposes datamodels that predict model outputs as a function of training data subsets, providing a framework for understanding data attribution through retraining experiments.

Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot

Introduces SISA (Sharded, Isolated, Sliced, Aggregated) training for efficient exact machine unlearning. Partitions data into shards with separate models, enabling targeted retraining when data must be forgotten.

Yinzhi Cao, Junfeng Yang

First formal definition of machine unlearning. Proposes converting learning algorithms into summation form to enable efficient data removal without full retraining. Foundational work establishing the unlearning problem.