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Existing Literature about Machine Unlearning

Machine Unlearning Papers

Awesome Github repo stars GitHub last commit

2023   2022   2021   2020   2019   2018   2017   < 2017  

2023

Author(s) Title Venue
Wang et al. KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment ACL
Yu et al. Unlearning Bias in Language Models by Partitioning Gradients ACL
Zhang et al. Machine Unlearning Methodology base on Stochastic Teacher Network ADMA
Wang et al. BFU: Bayesian Federated Unlearning with Parameter Self-Sharing Asia CCS
Ghazi et al. Ticketed Learning-Unlearning Schemes COLT
Chen et al. Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary CVPR
Lin et al. ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer CVPR
Hagos et al. Unlearning Spurious Correlations in Chest X-ray Classification Discovery Science
Lin et al. Machine Unlearning in Gradient Boosting Decision Trees KDD
Qian et al. Towards Understanding and Enhancing Robustness of Deep Learning Models against Malicious Unlearning Attacks KDD
Wu et al. Certified Edge Unlearning for Graph Neural Networks KDD
Cheng et al. GNNDelete: A General Strategy for Unlearning in Graph Neural Networks ICLR
Chien et al. Efficient Model Updates for Approximate Unlearning of Graph-Structured Data ICLR
Che et al. Fast Federated Machine Unlearning with Nonlinear Functional Theory ICML
Warnecke et al. Machine Unlearning for Features and Labels NDSS
Koch and Soll No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes SaTML
Schelter et al. Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation SIGIR
Wu et al. DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning SIGMOD
Wang et al. Inductive Graph Unlearning USENIX Security
Pan et al. Unlearning Graph Classifiers with Limited Data Resources WWW
Wu et al. GIF: A General Graph Unlearning Strategy via Influence Function WWW
Zhu et al. Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning WWW
Chen et al. Privacy preserving machine unlearning for smart cities Annals of Telemcommunications
Zhang et al. Machine Unlearning by Reversing the Continual Learning Applied Sciences
Deng et al. Vertical Federated Unlearning on the Logistic Regression Model Electronics
Zeng at al. Towards Highly-efficient and Accurate Services QoS Prediction via Machine Unlearning IEEE Access
Xia et al. FedME2: Memory Evaluation & Erase Promoting Federated Unlearning in DTMN IEEE Selected Areas in Communications
Zhang et al. Poison Neural Network-Based mmWave Beam Selection and Detoxification With Machine Unlearning IEEE Trans. on Comm.
Chundawat et al. Zero-Shot Machine Unlearning IEEE Trans. Info. Forensics and Security
Zhang et al. FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks IEEE Trans. Info. Forensics and Security
Tarun et al. Fast Yet Effective Machine Unlearning IEEE Trans. Neural Net. and Learn. Systems
Floridi Machine Unlearning: its nature, scope, and importance for a “delete culture” Philosophy & Technology
Zhang et al. A Review on Machine Unlearning SN Computer Science
Oesterling et al. Fair Machine Unlearning: Data Removal while Mitigating Disparities DMLR Workshop
Llamas et al. Effective Machine Learning-based Access Control Administration through Unlearning EuroS&PW
Bae et al. Gradient Surgery for One-shot Unlearning on Generative Model Generative AI & LAW Workshop
Alam et al. Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning arXiv
Cha et al. Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers arXiv
Cong and Mahdavi Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection arXiv
Dukler et al. SAFE: Machine Unlearning With Shard Graphs arXiv
Foster et al. Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening arXiv
Gandikota et al. Erasing Concepts from Diffusion Models arXiv
Huang et al. Tight Bounds for Machine Unlearning via Differential Privacy arXiv
Jia et al. Model Sparsification Can Simplify Machine Unlearning arXiv
Jin et al. Forgettable Federated Linear Learning with Certified Data Removal arXiv
Koh et al. Disposable Transfer Learning for Selective Source Task Unlearning arXiv
Krishna et al. Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten arXiv
Kurmanji et al. Towards Unbounded Machine Unlearning arXiv
LeBlond et al. Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile arXiv
Li and Ghosh Random Relabeling for Efficient Machine Unlearning arXiv
Li et al. Subspace based Federated Unlearning arXiv
Li et al. Selective and Collaborative Influence Function for Efficient Recommendation Unlearning arXiv
Li et al. Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data arXiv
Li et al. Federated Unlearning via Active Forgetting arXiv
Liu et al. Tangent Transformers for Composition, Privacy and Removal arXiv
Liu et al. Recommendation Unlearning via Matrix Correction arXiv
Moon et al. Feature Unlearning for Generative Models via Implicit Feedback arXiv
Poppi et al. Multi-Class Explainable Unlearning for Image Classification via Weight Filtering arXiv
Qu et al. Learn to Unlearn: A Survey on Machine Unlearning arXiv
Ramachandra and Sethi Machine Unlearning for Causal Inference arXiv
Shaik et al. Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy arXiv
Sun et al. Generative Adversarial Networks Unlearning arXiv
Tan et al. Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search arXiv
Xu et al. Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations arXiv
Xu and Teng Task-Aware Machine Unlearning and Its Application in Load Forecasting arXiv
Yamashita et al. One-Shot Machine Unlearning with Mnemonic Code arXiv
Zha et al. To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods arXiv
Zhang et al. Recommendation Unlearning via Influence Function arXiv
Zhang et al. Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions arXiv
Zhou et al. Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare bioRxiv
Jahanian et al. Protecting the Neural Networks against FGSM Attack Using Machine Unlearning Research Square
Su and Li Asynchronous Federated Unlearning
Fan Machine learning and unlearning for IoT anomaly detection

2022

Author(s) Title Venue
Marchant et al. Hard to Forget: Poisoning Attacks on Certified Machine Unlearning AAAI
Wu et al. PUMA: Performance Unchanged Model Augmentation for Training Data Removal AAAI
Dai et al. Knowledge Neurons in Pretrained Transformers ACL
Chen et al. Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning AISTATS
Nguyen et al. Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten ASIA CCS
Qian et al. Patient Similarity Learning with Selective Forgetting BIBM
Chen et al. Graph Unlearning CCS
Liu et al. Continual Learning and Private Unlearning CoLLAs
Mehta et al. Deep Unlearning via Randomized Conditionally Independent Hessians CVPR
Cao et al. Machine Unlearning Method Based On Projection Residual DSAA
Ye et al. Learning with Recoverable Forgetting ECCV
Thudi et al. Unrolling SGD: Understanding Factors Influencing Machine Unlearning EuroS&P
Becker and Liebig Certified Data Removal in Sum-Product Networks ICKG
Fu et al. Knowledge Removal in Sampling-based Bayesian Inference ICLR
Bevan and Atapour-Abarghouei Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification ICML
Hu et al. Membership Inference via Backdooring IJCAI
Yan et al. ARCANE: An Efficient Architecture for Exact Machine Unlearning IJCAI
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
Jiang et al. Machine Unlearning Survey MCTE
Zhang et al. Machine Unlearning for Image Retrieval: A Generative Scrubbing Approach MM
Tanno et al. Repairing Neural Networks by Leaving the Right Past Behind NeurIPS
Zhang et al. Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization NeurIPS
Gao et al. Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning PETS
Sommer et al. Athena: Probabilistic Verification of Machine Unlearning PoPETs
Lu et al. FP2-MIA: A Membership Inference Attack Free of Posterior Probability in Machine Unlearning ProvSec
Cao et al. FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information S&P
Ganhor et al. Unlearning Protected User Attributes in Recommendations with Adversarial Training SIGIR
Chen et al. Recommendation Unlearning TheWebConf
Zhou et al. Dynamically Selected Mixup Machine Unlearning TrustCom
Thudi et al. On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning USENIX Security
Wang et al. Federated Unlearning via Class-Discriminative Pruning WWW
Fan et al. Fast Model Update for IoT Traffic Anomaly Detection with Machine Unlearning IEEE IoT-J
Wu et al. Federated Unlearning: Guarantee the Right of Clients to Forget IEEE Network
Ma et al. Learn to Forget: Machine Unlearning Via Neuron Masking IEEE Trans. Dep. Secure Comp.
Lu et al. Label-only membership inference attacks on machine unlearning without dependence of posteriors Int. J. Intel. Systems
Meng et al. Active forgetting via influence estimation for neural networks Int. J. Intel. Systems
Baumhauer et al. Machine Unlearning: Linear Filtration for Logit-based Classifiers Machine Learning
Mahadaven and Mathiodakis Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study Machine Learning and Knowledge Extraction
Kong et al. Forgeability and Membership Inference Attacks AISec Workshop
Kim and Woo Efficient Two-Stage Model Retraining for Machine Unlearning CVPR Workshop
Gong et al. Forget-SVGD: Particle-Based Bayesian Federated Unlearning DSL Workshop
Chien et al. Certified Graph Unlearning GLFrontiers Workshop
Raunak and Menezes Rank-One Editing of Encoder-Decoder Models InterNLP Workshop
Lycklama et al. Cryptographic Auditing for Collaborative Learning ML Safety Workshop
Yoon et al. Few-Shot Unlearning SRML Workshop
Kong and Chaudhuri Data Redaction from Pre-trained GANs TSRML Workshop
Halimi et al. Federated Unlearning: How to Efficiently Erase a Client in FL? UpML Workshop
Rawat et al. Challenges and Pitfalls of Bayesian Unlearning UpML Workshop
Becker and Liebig Evaluating Machine Unlearning via Epistemic Uncertainty arXiv
Carlini et al. The Privacy Onion Effect: Memorization is Relative arXiv
Chilkuri et al. Debugging using Orthogonal Gradient Descent arXiv
Chourasia et al. Forget Unlearning: Towards True Data-Deletion in Machine Learning arXiv
Chundawat et al. Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher AAAI
Cohen et al. Control, Confidentiality, and the Right to be Forgotten arXiv
Eisenhofer et al. Verifiable and Provably Secure Machine Unlearning arXiv
Fraboni et al. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization arXiv
Gao et al. VeriFi: Towards Verifiable Federated Unlearning 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
Jang et al. Knowledge Unlearning for Mitigating Privacy Risks in Language Models arXiv
Kumar et al. Privacy Adhering Machine Un-learning in NLP arXiv
Liu et al. Forgetting Fast in Recommender Systems arXiv
Liu et al. Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning arXiv
Lu et al. Quark: Controllable Text Generation with Reinforced Unlearning arXiv
Malnick et al. Taming a Generative Model arXiv
Mercuri et al. An Introduction to Machine Unlearning arXiv
Mireshghallah et al. Non-Parametric Temporal Adaptation for Social Media Topic Classification arXiv
Nguyen et al. A Survey of Machine Unlearning arXiv
Pan et al. Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime arXiv
Pan et al. Machine Unlearning of Federated Clusters arXiv
Tarun et al. Deep Regression Unlearning ICML
Weng et al. Proof of Unlearning: Definitions and Instantiation arXiv
Wu et al. Federated Unlearning with Knowledge Distillation arXiv
Yu et al. LegoNet: A Fast and Exact Unlearning Architecture arXiv
Yoon et al. Few-Shot Unlearning by Model Inversion arXiv
Yuan et al. Federated Unlearning for On-Device Recommendation arXiv
Zhu et al. Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models arXiv
Cong and Mahdavi Privacy Matters! Efficient Graph Representation Unlearning with Data Removal Guarantee
Cong and Mahdavi GraphEditor: An Efficient Graph Representation Learning and Unlearning Approach
Wu et al. Provenance-based Model Maintenance: Implications for Privacy

2021

Author(s) Title Venue
Graves et al. Amnesiac Machine Learning AAAI
Yu et al. Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary AAAI
Izzo et al. Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations AISTATS
Li et al. Online Forgetting Process for Linear Regression Models AISTATS
Neel et al. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning ALT
Chen et al. REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data ASIA CCS
Chen et al. When Machine Unlearning Jeopardizes Privacy CCS
Ullah et al. Machine Unlearning via Algorithmic Stability COLT
Golatkar et al. Mixed-Privacy Forgetting in Deep Networks CVPR
Dang et al. Right to Be Forgotten in the Age of Machine Learning ICADS
Brophy and Lowd Machine Unlearning for Random Forests ICML
Huang et al. Unlearnable Examples: Making Personal Data Unexploitable ICLR
Goyal et al. Revisiting Machine Learning Training Process for Enhanced Data Privacy IC3
Tahiliani et al. Machine Unlearning: Its Need and Implementation Strategies IC3
Shibata et al. Learning with Selective Forgetting IJCAI
Liu et al. Federated Unlearning IWQoS
Huang et al. EMA: Auditing Data Removal from Trained Models MICCAI
Gupta et al. Adaptive Machine Unlearning NeurIPS
Khan and Swaroop Knowledge-Adaptation Priors NeurIPS
Sekhari et al. Remember What You Want to Forget: Algorithms for Machine Unlearning NeurIPS
Liu et al. FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models IWQoS
Bourtoule et al. Machine Unlearning S&P
Schelter et al. HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning SIGMOD
Gong et al. Bayesian Variational Federated Learning and Unlearning in Decentralized Networks SPAWC
Aldaghri et al. Coded Machine Unlearning IEEE Access
Liu et al. RevFRF: Enabling Cross-domain Random Forest Training with Revocable Federated Learning IEEE Trans. Dep. Secure Comp.
Wang and Schelter Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items ORSUM Workshop
Jose and Simeone A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization MLSP Workshop
Peste et al. SSSE: Efficiently Erasing Samples from Trained Machine Learning Models PRIML Workshop
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
Thudi et al. Bounding Membership Inference arXiv
Zeng et al. Learning to Refit for Convex Learning Problems arXiv

2020

Author(s) Title Venue
Tople te al. Analyzing Information Leakage of Updates to Natural Language Models CCS
Golatkar et al. Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks CVPR
Golatkar et al. Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations ECCV
Garg et al. Formalizing Data Deletion in the Context of the Right to be Forgotten EUROCRYPT
Guo et al. Certified Data Removal from Machine Learning Models ICML
Wu et al. DeltaGrad: Rapid Retraining of Machine Learning Models ICML
Nguyen et al. Variational Bayesian Unlearning NeurIPS
Liu et al. Learn to Forget: User-Level Memorization Elimination in Federated Learning researchgate
Felps et al. Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale arXiv
Sommer et al. Towards Probabilistic Verification of Machine Unlearning arXiv

2019

Author(s) Title Venue
Shintre et al. Making Machine Learning Forget APF
Du et al. Lifelong Anomaly Detection Through Unlearning CCS
Kim et al. Learning Not to Learn: Training Deep Neural Networks With Biased Data CVPR
Ginart et al. Making AI Forget You: Data Deletion in Machine Learning NeurIPS
Wang et al. Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks S&P
Chen et al. A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine Cluster Computing
Schelter “Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast AIDB Workshop

2018

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

2017

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

Before 2017

Author(s) Title Venue
Ganin et al. Domain-Adversarial Training of Neural Networks JMLR 2016
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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