Machine Unlearning Papers

2023   2022   2021   2020   2019   2018   2017   < 2017  

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2023

Author(s)	Title	Venue
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
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
Zeng at al.	Towards Highly-efficient and Accurate Services QoS Prediction via Machine Unlearning	IEEE Access
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
Jia et al.	Model Sparsification Can Simplify Machine Unlearning	arXiv
Jin et al.	Forgettable Federated Linear Learning with Certified Data Removal	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
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
Shaik et al.	Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy	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
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
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