Machine Unlearning Papers

2021

Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021.

Bourtoule et al. Machine Unlearning. In IEEE Symposium on Security and Privacy 2021.

Gupta et al. Adaptive Machine Unlearning. In Neurips 2021.

Huang et al. Unlearnable Examples: Making Personal Data Unexploitable. In ICLR 2021.

Neel et al. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. In ALT 2021.

Schelter et al. HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. In SIGMOD 2021.

Sekhari et al. Remember What You Want to Forget: Algorithms for Machine Unlearning. In Neurips 2021.

arXiv

Chen et al. Graph Unlearning. In arXiv 2021.

Chen et al. Machine unlearning via GAN. In arXiv 2021.

Fu et al. Bayesian Inference Forgetting. In arXiv 2021.

He et al. DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks. In arXiv 2021.

Khan and Swaroop. Knowledge-Adaptation Priors. In arXiv 2021.

Marchant et al. Hard to Forget: Poisoning Attacks on Certified Machine Unlearning . In arXiv 2021.

Parne et al. Machine Unlearning: Learning, Polluting, and Unlearning for Spam Email. In arXiv 2021.

Tarun et al. Fast Yet Effective Machine Unlearning . In arXiv 2021.

Ullah et al. Machine Unlearning via Algorithmic Stability. In arXiv 2021.

Wang et al. Federated Unlearning via Class-Discriminative Pruning . In arXiv 2021.

Warnecke et al. Machine Unlearning for Features and Labels. In arXiv 2021.

Zeng et al. Learning to Refit for Convex Learning Problems In arXiv 2021.

2020

Guo et al. Certified Data Removal from Machine Learning Models. In ICML 2020.

Golatkar et al. Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks. In CVPR 2020.

Wu et. al DeltaGrad: Rapid Retraining of Machine Learning Models. In ICML 2020.

arXiv

Aldaghri et al. Coded Machine Unlearning. In arXiv 2020.

Baumhauer et al. Machine Unlearning: Linear Filtration for Logit-based Classifiers. In arXiv 2020.

Garg et al. Formalizing Data Deletion in the Context of the Right to be Forgotten. In arXiv 2020.

Chen et al. When Machine Unlearning Jeopardizes Privacy. In arXiv 2020.

Felps et al. Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale. In arXiv 2020.

Golatkar et al. Mixed-Privacy Forgetting in Deep Networks. In arXiv 2020.

Golatkar et al. Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations. In arXiv 2020.

Izzo et al. Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations. In arXiv 2020.

Liu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning. In arXiv 2020.

Sommer et al. Towards Probabilistic Verification of Machine Unlearning. In arXiv 2020.

Yiu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning. In arXiv 2020.

Yu et al. Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary. In arXiv 2020.

2019

Chen et al. A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine. In Cluster Computing 2019.

Ginart et al. Making AI Forget You: Data Deletion in Machine Learning. In NeurIPS 2019.

Schelter. “Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast. In AIDB 2019.

Shintre et al. Making Machine Learning Forget. In APF 2019.

Du et al. Lifelong Anomaly Detection Through Unlearning. In CCS 2019.

Wang et al. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks. In IEEE Symposium on Security and Privacy 2019.

arXiv

Tople te al. Analyzing Privacy Loss in Updates of Natural Language Models. In arXiv 2019.

2018

Cao et al. Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning. In ASIACCS 2018.

European Union. GDPR, 2018.

State of California. California Consumer Privacy Act, 2018.

Veale et al. Algorithms that remember: model inversion attacks and data protection law. In The Royal Society 2018.

Villaronga et al. Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten. In Computer Law & Security Review 2018.

2017

Kwak et al. Let Machines Unlearn--Machine Unlearning and the Right to be Forgotten. In SIGSEC 2017.

Shokri et al. Membership Inference Attacks Against Machine Learning Models. In SP 2017.

Before 2017

Cao and Yang. Towards Making Systems Forget with Machine Unlearning. In IEEE Symposium on Security and Privacy 2015.

Tsai et al. Incremental and decremental training for linear classification. In KDD 2014.

Karasuyama and Takeuchi. Multiple Incremental Decremental Learning of Support Vector Machines. In NeurIPS 2009.

Duan et al. Decremental Learning Algorithms for Nonlinear Langrangian and Least Squares Support Vector Machines. In OSB 2007.

Romero et al. Incremental and Decremental Learning for Linear Support Vector Machines. In ICANN 2007.

Tveit et al. Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients. In DaWaK 2003.

Tveit and Hetland. Multicategory Incremental Proximal Support Vector Classifiers. In KES 2003.

Cauwenberghs and Poggio. Incremental and Decremental Support Vector Machine Learning. In NeurIPS 2001.

Canada. PIPEDA, 2000.