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Towards Making Systems Forget with Machine Unlearning

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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Hendrycks, D.; and Gimpel, K. 2017 · 2017

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Understanding black-box predictions via influence functions

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Making ai forget you: Data deletion in machine learning

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Eternal sunshine of the spotless net: Selective forgetting in deep networks

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Machine unlearning

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Measuring Massive Multitask Language Understanding

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Sekhari, A.; Acharya, J.; Kamath, G.; and Suresh, A. T. 2021 · 2021

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Machine unlearning of features and labels

Original

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Graph unlearning

Chen, M.; Zhang, Z.; Wang, T.; Backes, M.; Humbert, M.; and Zhang, Y. 2022 · 2022

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Federated Unlearning: How to Efficiently Erase a Client in FL?

Halimi, A.; Kadhe, S. R.; Rawat, A.; and Angel, N. B. 2022 · 2022

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Scaling Out-of-Distribution Detection for Real-World Settings

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Learn to forget: Machine unlearning via neuron masking

Ma, Z.; Liu, Y.; Liu, X.; Liu, J.; Ma, J.; and Ren, K. 2022 · 2022

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Pointer Sentinel Mixture Models

Merity, S.; Xiong, C.; Bradbury, J.; and Socher, R. 2022 · 2022

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A survey of machine unlearning

Original

Nguyen, T. T.; Huynh, T. T.; Nguyen, P. L.; Liew, A. W.-C.; Yin, H.; and Nguyen, Q. V. H. 2022 · 2022

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Out-of-distribution detection with deep nearest neighbors

Sun, Y.; Ming, Y.; Zhu, X.; and Li, Y. 2022 · 2022

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Unrolling sgd: Understanding factors influencing machine unlearning

Thudi, A.; Deza, G.; Chandrasekaran, V.; and Papernot, N. 2022 · 2022

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Federated Unlearning via Class-Discriminative Pruning

Wang, J.; Guo, S.; Xie, X.; and Qi, H. 2022 · 2022

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Mitigating neural network overconfidence with logit normalization

Wei, H.; Xie, R.; Cheng, H.; Feng, L.; An, B.; and Li, Y. 2022 · 2022

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LEACE: Perfect linear concept erasure in closed form

Belrose, N.; Schneider-Joseph, D.; Ravfogel, S.; Cotterell, R.; Raff, E.; and Biderman, S. 2023 · 2023

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Fast federated machine unlearning with nonlinear functional theory

Che, T.; Zhou, Y.; Zhang, Z.; Lyu, L.; Liu, J.; Yan, D.; Dou, D.; and Huan, J. 2023 · 2023

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Multimodal machine unlearning

Original

Cheng, J.; and Amiri, H. 2023 · 2023

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GNNDelete: A General Strategy for Unlearning in Graph Neural Networks

Cheng, J.; Dasoulas, G.; He, H.; Agarwal, C.; and Zitnik, M. 2023 · 2023

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Efficient Model Updates for Approximate Unlearning of Graph-Structured Data

Chien, E.; Pan, C.; and Milenkovic, O. 2023 · 2023

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Towards machine unlearning benchmarks: Forgetting the personal identities in facial recognition systems

Original

Choi, D.; and Na, D. 2023 · 2023

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Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation

Bui, T.-A.; Long, V.; Doan, K.; Le, T.; Montague, P.; Abraham, T.; and Phung, D. 2024 · 2024

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Learning to unlearn: Instance-wise unlearning for pre-trained classifiers

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Post-Training Attribute Unlearning in Recommender Systems

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Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice

Original

Cooper, A. F.; Choquette-Choo, C. A.; Bogen, M.; Jagielski, M.; Filippova, K.; Liu, K. Z.; Chouldechova, A.; Hayes, J.; Huang, Y.; Mireshghallah, N.; et al. 2024 · 2024

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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

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