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Machine Unlearning Papers

2022

Author(s) Title Venue
Chen et al. Recommendation Unlearning TheWebConf
Fu et al. Knowledge Removal in Sampling-based Bayesian Inference ICLR
Gao et al. Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning PETS
Liu et al. The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining INFOCOM
Liu et al. Backdoor Defense with Machine Unlearning INFOCOM
Marchant et al. Hard to Forget: Poisoning Attacks on Certified Machine Unlearning AAAI
Nguyen et al. Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten ASIA CCS
Wang et al. Federated Unlearning via Class-Discriminative Pruning WWW
Wu et al. PUMA: Performance Unchanged Model Augmentation for Training Data Removal AAAI
Yoon et al. Few-Shot Unlearning SRML Workshop
Chundawat et al. Zero-Shot Machine Unlearning arXiv
Dai et al. Knowledge Neurons in Pretrained Transformers arXiv
Goel et al. Evaluating Inexact Unlearning Requires Revisiting Forgetting arXiv
Guo et al. Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space arXiv
Guo et al. Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations arXiv
Liu et al. Continual and Private Unlearning arXiv
Tarun et al. Fast Yet Effective Machine Unlearning arXiv
Wu et al. Federated Unlearning with Knowledge Distillation arXiv

2021

Author(s) Title Venue
Aldaghri et al. Coded Machine Unlearning IEEE Access
Brophy and Lowd Machine Unlearning for Random Forests ICML
Bourtoule et al. Machine Unlearning S&P
Chen et al. When Machine Unlearning Jeopardizes Privacy CCS
Dang et al. Right to Be Forgotten in the Age of Machine Learning ICADS
Golatkar et al. Mixed-Privacy Forgetting in Deep Networks CVPR
Goyal et al. Revisiting Machine Learning Training Process for Enhanced Data Privacy IC3
Graves et al. Amnesiac Machine Learning AAAI
Gupta et al. Adaptive Machine Unlearning Neurips
Huang et al. Unlearnable Examples: Making Personal Data Unexploitable ICLR
Huang et al. EMA: Auditing Data Removal from Trained Models MICCAI
Khan and Swaroop Knowledge-Adaptation Priors NeurIPS
Liu et al. FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models IWQoS
Liu et al. RevFRF: Enabling Cross-domain Random Forest Training with Revocable Federated Learning IEEE Trans. Secure Dep. Comp.
Neel et al. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning ALT
Schelter et al. HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning SIGMOD
Sekhari et al. Remember What You Want to Forget: Algorithms for Machine Unlearning Neurips
Shibata et al. Learning with Selective Forgetting IJCAI
Jose and Simeone A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization MLSP Workshop
Tahiliani et al. Machine Unlearning: Its Need and Implementation Strategies IC3
Ullah et al. Machine Unlearning via Algorithmic Stability COLT
Wang and Schelter Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items ORSUM Workshop
Chen et al. Graph Unlearning arXiv
Chen et al. Machine unlearning via GAN arXiv
He et al. DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks arXiv
Madahaven and Mathioudakis Certifiable Machine Unlearning for Linear Models arXiv
Parne et al. Machine Unlearning: Learning, Polluting, and Unlearning for Spam Email arXiv
Peste et al. SSSE: Efficiently Erasing Samples from Trained Machine Learning Models arXiv
Tarun et al. Fast Yet Effective Machine Unlearning arXix
Thudi et al. Unrolling SGD: Understanding Factors Influencing Machine Unlearning arXiv
Thudi et al. On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning arXiv
Thudi et al. Bounding Membership Inference arXiv
Wang et al. Federated Unlearning via Class-Discriminative Pruning arXiv
Warnecke et al. Machine Unlearning for Features and Labels arXiv
Zeng et al. Learning to Refit for Convex Learning Problems arXiv

2020

Author(s) Title Venue
Garg et al. Formalizing Data Deletion in the Context of the Right to be Forgotten EUROCRYPT
Golatkar et al. Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations ECCV
Golatkar et al. Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks CVPR
Guo et al. Certified Data Removal from Machine Learning Models ICML
Nguyen et al. Variational Bayesian Unlearning NeurIPS
Tople te al. Analyzing Information Leakage of Updates to Natural Language Models CCS
Wu et al. DeltaGrad: Rapid Retraining of Machine Learning Models ICML
Baumhauer et al. Machine Unlearning: Linear Filtration for Logit-based Classifiers arXiv
Felps et al. Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale arXiv
Izzo et al. Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations arXiv
Li et al. Online Forgetting Process for Linear Regression Models arXiv
Liu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning arXiv
Sommer et al. Towards Probabilistic Verification of Machine Unlearning arXiv
Yu et al. Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary arXiv

2019

Author(s) Title Venue
Chen et al. A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine Cluster Computing
Ginart et al. Making AI Forget You: Data Deletion in Machine Learning NeurIPS
Schelter “Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast AIDB
Shintre et al. Making Machine Learning Forget APF
Du et al. Lifelong Anomaly Detection Through Unlearning CCS
Wang et al. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks S&P

2018

Author(s) Title Venue
Cao et al. Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning ASIACCS
European Union GDPR
State of California California Consumer Privacy Act
Veale et al. Algorithms that remember: model inversion attacks and data protection law The Royal Society
Villaronga et al. Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten Computer Law & Security Review

2017

Author(s) Title Venue
Kwak et al. Let Machines Unlearn--Machine Unlearning and the Right to be Forgotten SIGSEC
Shokri et al. Membership Inference Attacks Against Machine Learning Models S&P

Before 2017

Author(s) Title Venue
Cao and Yang Towards Making Systems Forget with Machine Unlearning S&P 2015
Tsai et al. Incremental and decremental training for linear classification KDD 2014
Karasuyama and Takeuchi Multiple Incremental Decremental Learning of Support Vector Machines NeurIPS 2009
Duan et al. Decremental Learning Algorithms for Nonlinear Langrangian and Least Squares Support Vector Machines OSB 2007
Romero et al. Incremental and Decremental Learning for Linear Support Vector Machines ICANN 2007
Tveit et al. Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients DaWaK 2003
Tveit and Hetland Multicategory Incremental Proximal Support Vector Classifiers KES 2003
Cauwenberghs and Poggio Incremental and Decremental Support Vector Machine Learning NeurIPS 2001
Canada PIPEDA 2000

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