Machine Unlearning Papers and Benchmarks

Frameworks

OpenUnlearning

Machine Unlearning Comparator

Papers

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2025

Author(s)	Title	Venue
Jiang et al.	Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models	AAAI
Han et al.	DuMo: Dual Encoder Modulation Network for Precise Concept Erasure	AAAI
Wu et al.	Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient	AAAI
Wang et al.	Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models	AAAI
Yuan et al.	Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models	AAAI
Jin et al.	Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate	ACL
Yang et al.	CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP	ACL
Choi et al.	Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport	ACL
Bhaila et al.	Soft Prompting for Unlearning in Large Language Models	ACL
Sun et al.	Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race	ACL
Xu et al.	ReLearn: Unlearning via Learning for Large Language Models	ACL
Huo et al.	MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models	ACL
Liu et al.	Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models	ACL
Tran et al.	Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training	ACL
Zhuang et al.	SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?	ACL
Liu et al.	Rethinking Machine Unlearning in Image Generation Models	ACM CCS
Chowdhury et al.	Fundamental Limits of Perfect Concept Erasure	AISTATS
Xue et al.	CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models	BMVC
Mekala et al.	Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models	COLING
Ma et al.	Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis	COLING
Sanyal et al.	Agents Are All You Need for LLM Unlearning	COLM
Zhou et al.	Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks	CVPR
Li et al.	Detect-and-Guide: Self-regulation of Diffusion Models for Safe Text-to-Image Generation via Guideline Token Optimization	CVPR
Wang et al	Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters	CVPR
Wang et al.	ACE: Anti-Editing Concept Erasure in Text-to-Image Models	CVPR
Wu et al.	EraseDiff: Erasing Data Influence In Diffusion Models	CVPR
Lee et al.	ESC: Erasing Space Concept for Knowledge Deletion	CVPR
Thakral et al.	Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models	CVPR
Srivatsan et al.	STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models	CVPR
Lee et al.	Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation	CVPR
Shirkavand et al.	Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models	CVPR
Pan et al.	Multi-Objective Large Language Model Unlearning	ICASSP
Wang et al.	Large Scale Knowledge Washing	ICLR
Koulischer et al.	Dynamic Negative Guidance of Diffusion Models	ICLR
Feng et al.	Controllable Unlearning for Image-to-Image Generative Models via epsilon-Constrained Optimization	ICLR
Ding et al.	Unified Parameter-Efficient Unlearning for LLMs	ICLR
Jin et al.	Unlearning as Multi-Task Optimization: a normalized gradient difference approach with adaptive learning rate	ICLR
Farrell et al.	Applying Sparse Autoencoders to Unlearn Knowledge in Language Models	ICLR
Cywinski et al.	SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders	ICLR
Yoon et al.	SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation	ICLR
Choi et al.	Unlearning-based Neural Interpretations	ICLR
Di et al.	Adversarial Machine Unlearning	ICLR
Sakarvadia et al.	Mitigating Memorization in Language Models	ICLR
Li et al.	When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers	ICLR
Scholten et al.	A Probabilistic Perspective on Unlearning and Alignment for Large Language Models	ICLR
Zhang et al.	Catastrophic Failure of LLM Unlearning via Quantization	ICLR
Cha et al.	Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs	ICLR
Shi et al.	MUSE: Machine Unlearning Six-Way Evaluation for Language Models	ICLR
Bui et al.	Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them	ICLR
Yuan et al.	A Closer Look at Machine Unlearning for Large Language Models	ICLR
Du et al.	Textual Unlearning Gives a False Sense of Unlearning	ICML
Li et al.	One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework	ICML
Karvonen et al.	SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability	ICML
Zhang et al.	Minimalist Concept Erasure in Generative Models	ICML
Fan et al.	Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond	ICML
Pathak et al.	Quantum-Inspired Audio Unlearning: Towards Privacy-Preserving Voice Biometrics	IJCB
Dou et al.	Avoiding Copyright Infringement via Large Language Model Unlearning	NAACL
Liu et al.	Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench	NAACL
Dong et al.	UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models	NAACL
Ye et al.	Reinforcement Unlearning	NDSS
Bother et al.	Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection	PACMMOD
Thaker et al.	Position: LLM Unlearning Benchmarks are Weak Measures of Progress	SaTML
Xia et al.	Edge Unlearning is Not "on Edge"! an Adaptive Exact Unlearning System on Resource-Constrained Devices	SP
Wang et al.	Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness	USENIX Security
Wang et al.	TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning	WWW
Justicia et al.	Digital forgetting in large language models: a survey of unlearning methods	Artificial Intelligence Review
Qu et al.	The Frontier of Data Erasure: A Survey on Machine Unlearning for Large Language Models	Computer
Liu et al.	Threats, Attacks, and Defenses in Machine Unlearning: A Survey	IEEE Open Journal of the Computer Society
Sun et al.	Generative Adversarial Networks Unlearning	IEEE Transactions on Dependable and Secure Computing
Zuo et al.	Machine unlearning through fine-grained model parameters perturbation	IEEE Transactions on Knowledge and Data Engineering
Li et al.	Class-wise federated unlearning: Harnessing active forgetting with teacher–student memory generation	Knowledge-Based Systems
Liu et al.	Rethinking Machine Unlearning for Large Language Models	Nature Machine Intelligence
Cooper et al.	Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice	SSRN
Tiwary et al.	Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks	TMLR
MIranda et al.	Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions	TMLR
Huang et al.	Offset Unlearning for Large Language Models	TMLR
Sinha et al.	UnSTAR: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs	TMLR
Che et al.	Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities	TMLR
Vidal et al.	Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU	CVPR Workshop
Cai et al.	AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security	ICLR Workshop
Kim et al.	Training-Free Safe Denoisers For Safe Use of Diffusion Models	ICLR Workshop
Bui et al.	Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts	ICLR Workshop
Sanga et al.	Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning	ICML Workshop
Wu et al.	Evaluating Deep Unlearning in Large Language Models	ICML Workshop
Spohn et al.	Align-then-Unlearn: Embedding Alignment for LLM Unlearning	ICML Workshop
Dosajh et al.	Unlearning Factual Knowledge from LLMs Using Adaptive RMU	SemEval
Xu et al.	Unlearning via Model Merging	SemEval
Bronec et al.	Low-Rank Negative Preference Optimization	SemEval
Srivasthav P et al.	Forgotten but Not Lost: The Balancing Act of Selective Unlearning in Large Language Models	SemEval
Premptis et al.	Parameter-Efficient Unlearning for Large Language Models using Data Chunking	SemEval
Kim et al.	Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols	arxiv
Kwak et al.	NegMerge: Consensual Weight Negation for Strong Machine Unlearning	arxiv
Wang et al.	GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models	arxiv
Geng et al.	A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models	arxiv
Barez et al.	Open Problems in Machine Unlearning for AI Safety	arxiv
Fan et al.	Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning	arxiv
Staufer et al.	What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests	arxiv
Yeats et al.	Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge	arxiv
Xiong et al.	The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation	arxiv
Scholten et al.	Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs	arxiv
Han et al.	Unlearning the Noisy Correspondence Makes CLIP More Robust	arxiv
Kawakami et al.	PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning	arxiv
Ma et al.	SoK: Semantic Privacy in Large Language Models	arxiv
Rezaei et al.	Model State Arithmetic for Machine Unlearning	arxiv
Sinha et al.	Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models	arxiv
Zhang et al.	Does Multimodal Large Language Model Truly Unlearn? Stealthy MLLM Unlearning Attack	arxiv
Jiang et al.	Large Language Model Unlearning for Source Code	arxiv
Hu et al.	BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap	arxiv
Wu et al.	Learning-Time Encoding Shapes Unlearning in LLMs	arxiv
Chen et al.	Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs	arxiv
Wang et al.	Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills	arxiv
Songdej et al.	Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization	arxiv
Suriyakumar et al.	UCD: Unlearning in LLMs via Contrastive Decoding	arxiv
Ma et al.	GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models	arxiv
Ren et al.	SoK: Machine Unlearning for Large Language Models	arxiv
Reisizadeh et al.	BLUR: A Bi-Level Optimization Approach for LLM Unlearning	arxiv
Ye et al.	LLM Unlearning Should Be Form-Independent	arxiv
Zhang et al.	RULE: Reinforcement UnLEarning Achieves Forget-Retain Pareto Optimality	arxiv
Lee et al.	Distillation Robustifies Unlearning	arxiv
Wang et al.	Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness	arxiv
Wei et al.	Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness	arxiv
Entesari et al.	Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models	arxiv
Wen et al.	Quantifying Cross-Modality Memorization in Vision-Language Models	arxiv
Chen et al.	Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning	arxiv
Zhou et al.	Not All Tokens Are Meant to Be Forgotten	arxiv
Kim et al.	Rethinking Post-Unlearning Behavior of Large Vision-Language Models	arxiv
Wang et al.	Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning	arxiv
Wan et al.	Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning	arxiv
Feng et al.	Existing Large Language Model Unlearning Evaluations Are Inconclusive	arxiv
Wang et al.	Model Unlearning via Sparse Autoencoder Subspace Guided Projections	arxiv
Wu et al.	Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models	arxiv
Chen et al.	Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs	arxiv
Siddiqui et al.	From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization	arxiv
Li et al.	Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?	arxiv
Jiang et al.	Graceful Forgetting in Generative Language Models	arxiv
Shi et al.	Safety Alignment via Constrained Knowledge Unlearning	arxiv
Ye et al.	T2VUnlearning: A Concept Erasing Method for Text-to-Video Diffusion Models	arxiv
To et al.	Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models via Automated Adversarial Prompting	arxiv
Xu et al.	Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs	arxiv
Lee et al.	Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models	arxiv
Ma et al.	Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value	arxiv
Yu et al.	UniErase: Unlearning Token as a Universal Erasure Primitive for Language Models	arxiv
Yoon et al.	R-TOFU: Unlearning in Large Reasoning Models	arxiv
Jeung et al.	DUSK: Do Not Unlearn Shared Knowledge	arxiv
Jeung et al.	SEPS: A Separability Measure for Robust Unlearning in LLMs	arxiv
Deng et al.	GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection	arxiv
Yang et al.	Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning	arxiv
Qian et al.	Layered Unlearning for Adversarial Relearning	arxiv
Vasilev et al.	Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation	arxiv
Lu et al.	WaterDrum: Watermarking for Data-centric Unlearning Metric	arxiv
Xu et al.	OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models	arxiv
Sun et al.	Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?	arxiv
Patil et al.	Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation	arxiv
Zhong et al.	DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers	arxiv
Chen et al.	ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data	arxiv
Mahmud et al.	DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs	arxiv
Klochkov et al.	A mean teacher algorithm for unlearning of language models	arxiv
Kim et al.	GRAIL: Gradient-Based Adaptive Unlearning for Privacy and Copyright in LLMs	arxiv
Pal et al.	LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks	arxiv
Muhamed et al.	SAEs Can Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs	arxiv
Feng et al.	Bridging the Gap Between Preference Alignment and Machine Unlearning	arxiv
Feng et al.	A Neuro-inspired Interpretation of Unlearning in Large Language Models through Sample-level Unlearning Difficulty	arxiv
Krishnan et al.	Not All Data Are Unlearned Equally	arxiv
Kuo et al.	Exact Unlearning of Finetuning Data via Model Merging at Scale	arxiv
Xu et al.	SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning	arxiv
Li et al.	Effective Skill Unlearning through Intervention and Abstention	arxiv
Xu et al.	PEBench: A Fictitious Dataset to Benchmark Machine Unlearning for Multimodal Large Language Models	arxiv
Poppi et al.	Hyperbolic Safety-Aware Vision-Language Models	arxiv
Chen et al.	Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning	arxiv
Wang et al.	UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets	arxiv
Zhao et al.	Improving LLM Safety Alignment with Dual-Objective Optimization	arxiv
Yang et al.	CE-U: Cross Entropy Unlearning	arxiv
Wang et al.	Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models	arxiv
Wang et al.	Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond	arxiv
Yang et al.	FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge	arxiv
Jiang et al.	Holistic Audit Dataset Generation for LLM Unlearning via Knowledge Graph Traversal and Redundancy Removal	arxiv
Chen et al.	Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models	arxiv
Jung et al.	CoME: An Unlearning-based Approach to Conflict-free Model Editing	arxiv
Ramakrishna et al.	LUME: LLM Unlearning with Multitask Evaluations	arxiv
Patil et al.	UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning	arxiv
Russinovich et al.	Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models	arxiv
Chen et al.	SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning	arxiv
Chang et al.	Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning	arxiv
Shen et al.	LUNAR: LLM Unlearning via Neural Activation Redirection	arxiv
Geng et al.	Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning	arxiv
Hu et al.	FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model	arxiv
Cheng et al.	Tool Unlearning for Tool-Augmented LLMs	arxiv
Zhang et al.	Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating	arxiv
Huu-Tien et al.	Improving LLM Unlearning Robustness via Random Perturbations	arxiv
He et al.	Deep Contrastive Unlearning for Language Models	arxiv
Khoriaty et al.	Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning	arxiv
Ren et al.	A General Framework to Enhance Fine-tuning-based LLM Unlearning	arxiv
Lang et al.	Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis	arxiv
Amara et al.	EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques	arxiv
Brannvall et al.	Technical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning	arxiv
Chen et al.	Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion Models	arxiv
Fuchi et al.	Erasing with Precision: Evaluating Specific Concept Erasure from Text-to-Image Generative Models	arxiv
Kim et al.	A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models	arxiv
Meng et al.	Concept Corrector: Erase concepts on the fly for text-to-image diffusion models	arxiv
Beerens et al.	On the Vulnerability of Concept Erasure in Diffusion Models	arxiv
Chen et al.	TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models	arxiv
Li et al.	SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models	arxiv
Tian et al.	Sparse Autoencoder as a Zero-Shot Classifier for Concept Erasing in Text-to-Image Diffusion Models	arxiv
Carter et al.	ACE: Attentional Concept Erasure in Diffusion Models	arxiv
Li et al.	Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts	arxiv
Grebe et al.	Erased but Not Forgotten: How Backdoors Compromise Concept Erasure	arxiv
Gao et al.	Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models	arxiv
Biswas et al.	CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models	arxiv
Chen et al.	Comprehensive Evaluation and Analysis for NSFW Concept Erasure in Text-to-Image Diffusion Models	arxiv
Liu et al.	Erased or Dormant? Rethinking Concept Erasure Through Reversibility	arxiv
Lu et al.	When Are Concepts Erased From Diffusion Models?	arxiv
Xie et al.	Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression	arxiv
Gur-Arieh et al.	Precise In-Parameter Concept Erasure in Large Language Models	arxiv
Carter et al.	TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models	arxiv
Zhu et al.	SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment Erasing	arxiv
Fan et al.	EAR: Erasing Concepts from Unified Autoregressive Models	arxiv
Lee et al.	Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate	arxiv
Fu et al.	FADE: Adversarial Concept Erasure in Flow Models	arxiv
Wu et al.	MUNBa: Machine Unlearning via Nash Bargaining	arxiv

2024

Author(s)	Title	Venue
Tian et al.	DeRDaVa: Deletion-Robust Data Valuation for Machine Learning	AAAI
Ni et al.	ORES: open-vocabulary responsible visual synthesis	AAAI
Moon et al.	Feature Unlearning for Pre-trained GANs and VAEs	AAAI
Rashid et al.	Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage	AAAI
Cha et al.	Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers	AAAI
Hong et al.	All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models	AAAI
Kim et al.	Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation	AAAI
Foster et al.	Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening	AAAI
Hu et al.	Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation	AAAI
Li et al.	Towards Effective and General Graph Unlearning via Mutual Evolution	AAAI
Liu et al.	Backdoor Attacks via Machine Unlearning	AAAI
You et al.	RRL: Recommendation Reverse Learning	AAAI
Moon et al.	Feature Unlearning for Generative Models via Implicit Feedback	AAAI
Li et al.	SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Models	ACM CCS
Lin et al.	GDR-GMA: Machine Unlearning via Direction-Rectified and Magnitude-Adjusted Gradients	ACM MM
Huang et al.	Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials	ACM SE
Feng et al.	Fine-grained Pluggable Gradient Ascent for Knowledge Unlearning in Language Models	ACL
Arad et al.	ReFACT: Updating Text-to-Image Models by Editing the Text Encoder	ACL
Wu et al.	Universal Prompt Optimizer for Safe Text-to-Image Generation	ACL
Liu et al.	Towards Safer Large Language Models through Machine Unlearning	ACL
Kim et al.	Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning	ACL
Lee et al.	Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models	ACL
Choi et al.	Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models	ACL
Isonuma et al.	Unlearning Traces the Influential Training Data of Language Models	ACL
Zhou et al.	Visual In-Context Learning for Large Vision-Language Models	ACL
Xing et al.	EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models	ACL
Yao et al.	Machine Unlearning of Pre-trained Large Language Models	ACL
Zhao et al.	Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning	ACL
Ni et al.	Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models	ACL
Zhou et al.	Making Harmful Behaviors Unlearnable for Large Language Models	ACL
Yamashita et al.	One-Shot Machine Unlearning with Mnemonic Code	ACML
Fraboni et al.	SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization	AISTATS
Alshehri and Zhang	Forgetting User Preference in Recommendation Systems with Label-Flipping	BigData
Qiu et al.	FedCIO: Efficient Exact Federated Unlearning with Clustering, Isolation, and One-shot Aggregation	BigData
Yang and Li	When Contrastive Learning Meets Graph Unlearning: Graph Contrastive Unlearning for Link Prediction	BigData
Hu et al.	ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach	CCS
Zhang et al.	Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning	COLM
Maini et al.	TOFU: A Task of Fictitious Unlearning for LLMs	COLM
Abbasi et al.	Brainwash: A Poisoning Attack to Forget in Continual Learning	CVPR
Chen et al.	Towards Memorization-Free Diffusion Models	CVPR
Lyu et al.	One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications	CVPR
Wallace et al.	Diffusion Model Alignment Using Direct Preference Optimization	CVPR
Lu et al.	MACE: Mass Concept Erasure in Diffusion Models	CVPR
Chen et al.	WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models	ECAI
Fan et al.	Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning	ECCV
Gong et al.	Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models	ECCV
Kim et al.	R.A.C.E. : Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model	ECCV
Kim et al.	Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion	ECCV
Wu et al.	Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks	ECCV
Zhang et al.	To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now	ECCV
Liu et al.	Implicit Concept Removal of Diffusion Models	ECCV
Ban et al.	Understanding the Impact of Negative Prompts: When and How Do They Take Effect?	ECCV
Zhang et al.	IMMA: Immunizing Text-to-Image Models Against Malicious Adaptation	ECCV
Poppi et al.	Removing NSFW Concepts from Vision-and-Language Models for Text-to-Image Retrieval and Generation	ECCV
Liu et al.	Latent Guard: A Safety Framework for Text-to-Image Generation	ECCV
Huang et al.	Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers	ECCV
Cheng et al.	MultiDelete for Multimodal Machine Unlearning	ECCV
Wang et al.	How to Forget Clients in Federated Online Learning to Rank?	ECIR
Jia et al.	SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning	EMNLP
Joshi et al.	Towards Robust Evaluation of Unlearning in LLMs via Data Transformations	EMNLP
Tian et al.	To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models	arxiv
Chakraborty et al.	Can Textual Unlearning Solve Cross-Modality Safety Alignment?	EMNLP
Huang et al.	Demystifying Verbatim Memorization in Large Language Models	EMNLP
Liu et al.	Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective	EMNLP
Chen et al.	Unlearn What You Want to Forget: Efficient Unlearning for LLMs	EMNLP
Liu et al.	Forgetting Private Textual Sequences in Language Models Via Leave-One-Out Ensemble	ICASSP
Liu et al.	Learning to Refuse: Towards Mitigating Privacy Risks in LLMs	ICCL
Fan et al.	SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation	ICLR
Liu et al.	Tangent Transformers for Composition, Privacy and Removal	ICLR
Li et al.	Machine Unlearning for Image-to-Image Generative Models	ICLR
Shen et al.	Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models	ICLR
Li et al.	Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models	ICLR
Tsai et al.	Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?	ICLR
Wang et al.	A Unified and General Framework for Continual Learning	ICLR
Shi et al.	Detecting Pretraining Data from Large Language Models	ICLR
Eldan et al.	Who’s Harry Potter? Approximate Unlearning in LLMs	ICLR
Wang et al.	LLM Unlearning via Loss Adjustment with Only Forget Data	ICLR
Chavhan et al.	ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning	ICLR
Zhao et al.	Rethinking Adversarial Robustness in the Context of the Right to be Forgotten	ICML
Pawelczyk et al.	In-Context Unlearning: Language Models As Few Shot Unlearners	ICML
Barbulescu et al.	To each (textual sequence) its own: improving memorized-data unlearning in large language models	ICML
Li et al.	The WMDP benchmark: measuring and reducing malicious use with unlearning	ICML
Das et al.	Larimar: large language models with episodic memory control	ICML
Barbulescu et al.	To each (textual sequence) its own: improving memorized-data unlearning in large language models	ICML
Zhao et al.	Learning and forgetting unsafe examples in large language models	ICML
Basu et al.	On mechanistic knowledge localization in text-to-image generative models	ICML
Zhang et al.	SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning	ICPR
Cai et al.	Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation	KDD
Gong et al.	A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction	KDD
Xue et al.	Erase to Enhance: Data-Efficient Machine Unlearning in MRI Reconstruction	MIDL
Gao et al.	Ethos: Rectifying Language Models in Orthogonal Parameter Space	NAACL
Park et al.	Direct Unlearning Optimization for Robust and Safe Text-to-Image Models	NeurIPS
Ko et al.	Boosting Alignment for Post-Unlearning Text-to-Image Generative Models	NeurIPS
Yang et al.	GuardT2I: Defending Text-to-Image Models from Adversarial Prompts	NeurIPS
Li et al.	Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models	NeurIPS
Jain et al.	What Makes and Breaks Safety Fine-tuning? A Mechanistic Study	NeurIPS
Wu et al.	Cross-model Control: Improving Multiple Large Language Models in One-time Training	NeurIPS
Bui et al.	Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation	NeurIPS
Zhao et al.	What makes unlearning hard and what to do about it	NeurIPS
Zhang et al.	Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models	NeurIPS
Yao et al.	Large Language Model Unlearning	NeurIPS
Ji et al.	Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference	NeurIPS
Liu et al.	Large Language Model Unlearning via Embedding-Corrupted Prompts	NeurIPS
Jia et al.	WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models	NeurIPS
Zhang et al.	UnlearnCanvas: A Stylized Image Dataset to Benchmark Machine Unlearning for Diffusion Models	NeurIPS D&B
Jin et al.	RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models	NeurIPS D&B
Kurmanji et al.	Machine Unlearning in Learned Databases: An Experimental Analysis	SIGMOD
Shen et al.	CaMU: Disentangling Causal Effects in Deep Model Unlearning	SDM
Yoon et al.	Few-Shot Unlearning	SP
Hu et al.	Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning	SP
Hoang et al.	Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection	WACV
Gandikota et al.	Unified Concept Editing in Diffusion Models	WACV
Malnick et al.	Taming Normalizing Flows	WACV
Xin et al.	On the Effectiveness of Unlearning in Session-Based Recommendation	WSDM
Zhang	Graph Unlearning with Efficient Partial Retraining	WWW
Liu et al.	Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning	WWW
Liu et al.	A Survey on Federated Unlearning: Challenges, Methods, and Future Directions	ACM Computing Surveys
Zhang et al.	Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions	AI and Ethics
Zha et al.	To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods	AI and Ethics
Zhang et al.	Recommendation Unlearning via Influence Function	ACM Transactions on Recommender Systems
Schoepf et al.	Potion: Towards Poison Unlearning	DMLR
Wang et al.	Towards efficient and effective unlearning of large language models for recommendation	Frontiers of Computer Science
Poppi et al.	Multi-Class Explainable Unlearning for Image Classification via Weight Filtering	IEEE Intelligent Systems
Panda and AP	FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models	IEEE Transactions on Artificial Intelligence
Alam et al.	Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning	IEEE Transactions on Artificial Intelligence
Shaik et al.	FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning	IEEE Transactions on Knowledge and Data Engineering
Shaik et al.	Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy	IEEE Transactions on Neural Networks and Learning Systems
Romandini et al.	Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics	IEEE Transactions on Neural Networks and Learning Systems
Xu and Teng	Task-Aware Machine Unlearning and Its Application in Load Forecasting	IEEE Transactions on Power Systems
Li et al.	Pseudo Unlearning via Sample Swapping with Hash	Information Science
Fore et al.	Unlearning Climate Misinformation in Large Language Models	ClimateNLP
Zhang et al.	Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models	CVPR Workshop
Shi et al.	DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal	ECCV Workshop
Sridhar et al.	Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models	ECCV Workshop
Schoepf et al.	Loss-Free Machine Unlearning	ICLR Tiny Paper
Tamirisa et al.	Toward Robust Unlearning for LLMs	ICLR Workshop
Sun et al.	Learning and Unlearning of Fabricated Knowledge in Language Models	ICML Workshop
Wang et al.	Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing	ICML Workshop
Kadhe et al.	Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs	ICML Workshop
Zhao et al.	Scalability of memorization-based machine unlearning	NeurIPS Workshop
Wu et al.	CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept	NeurIPS Workshop
Cheng et al.	MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning	NeurIPS Workshop
Seyitoğlu et al.	Extracting Unlearned Information from LLMs with Activation Steering	NeurIPS Workshop
Wei et al.	Provable unlearning in topic modeling and downstream tasks	NeurIPS Workshop
Lucki et al.	An Adversarial Perspective on Machine Unlearning for AI Safety	NeurIPS Workshop
Li et al.	LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet	NeurIPS Workshop
Smirnov et al.	Classifier-free guidance in LLMs Safety	NeurIPS Workshop
Liu et al.	Machine Unlearning in Generative AI: A Survey	arxiv
Xu	Machine Unlearning for Traditional Models and Large Language Models: A Short Survey	arxiv
Lynch et al.	Eight Methods to Evaluate Robust Unlearning in LLMs	arxiv
Dontsov et al.	CLEAR: Character Unlearning in Textual and Visual Modalities	arXiv
Hong et al.	Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces	arXiv
Jung et al.	Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization	arXiv
Pham et al.	Robust Concept Erasure Using Task Vectors	arXiv
Qian et al.	Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten	arXiv
Schoepf et al.	An Information Theoretic Approach to Machine Unlearning	arxiv
Schoepf et al.	Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening	arXiv
Zhao et al.	Separable Multi-Concept Erasure from Diffusion Models	arXiv
Dige et al.	Mitigating Social Biases in Language Models through Unlearning	arxiv
Hong et al.	Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces	arxiv
Wang et al.	Towards Effective Evaluations and Comparisons for LLM Unlearning Methods	arxiv
Ashuach et al.	REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space	arxiv
Zuo et al.	Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning	arxiv
Wang et al.	RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models	arxiv
Chen e tal.	Machine Unlearning in Large Language Models	arxiv
Lu et al.	Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge	arxiv
Stoehr et al.	Localizing Paragraph Memorization in Language Models	arxiv
Pochinkov et al.	Dissecting Language Models: Machine Unlearning via Selective Pruning	arxiv
Gu et al.	Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models	arxiv
Thaker et al.	Guardrail Baselines for Unlearning in LLMs	arxiv
Wang et al.	When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?	arxiv
Muresanu et al.	Unlearnable Algorithms for In-context Learning	arxiv
Zhao et al.	Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation	arxiv
Choi et al.	Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning	arxiv
Guo et al.	Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization	arxiv
Deeb et al.	Do Unlearning Methods Remove Information from Language Model Weights?	arxiv
Takashiro et al.	Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning	arxiv
Veldanda et al.	LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models	arxiv
Gu et al.	MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts	arxiv
Zhang et al.	Unforgettable Generalization in Language Models	arxiv
Kazemi et al.	Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code	arxiv
Huu-Tien et al.	On Effects of Steering Latent Representation for Large Language Model Unlearning	arxiv
Yang et al.	Hotfixing Large Language Models for Code	arxiv
Lizzo et al.	UNLEARN Efficient Removal of Knowledge in Large Language Models	arxiv
Tamirisa et al.	Tamper-Resistant Safeguards for Open-Weight LLMs	arxiv
Zhou et al.	On the Limitations and Prospects of Machine Unlearning for Generative AI	arxiv
Tang et al.	Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models	arxiv
Lu et al.	Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation	arxiv
Gao et al.	On Large Language Model Continual Unlearning	arxiv
Kolbeinsson et al.	Composable Interventions for Language Models	arxiv
Hernandez et al.	If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers	arxiv
Zhang et al.	From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks	arxiv
Scaria et al.	Can Small Language Models Learn, Unlearn, and Retain Noise Patterns?	arxiv
Shumailov et al.	UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI	arxiv
Qiu et al.	How Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspective	arxiv
Lu et al.	Learn and Unlearn in Multilingual LLMs	arxiv
Ma et al.	Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset	arxiv
Rezaei et al.	RESTOR: Knowledge Recovery in Machine Unlearning	arxiv
Baluta et al.	Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method	arxiv
Doshi et al.	Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods	arxiv
Wei et al.	Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning	arxiv
Zuo et al.	Large Language Model Federated Learning with Blockchain and Unlearning for Cross-Organizational Collaboration	arxiv
Dou et al.	Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning	arxiv
Ren et al.	Copyright Protection in Generative AI: A Technical Perspective, 2024	arxiv
Gu et al.	Second-Order Information Matters: Revisiting Machine Unlearning for Large Language Models	arxiv
Chakraborty et al.	Cross-Modal Safety Alignment: Is textual unlearning all you need?	arxiv
Liang et al.	Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning	arxiv
Wu et al.	Erasing Undesirable Influence in Diffusion Models	arxiv
Gao et al.	Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts	arxiv
Huang et al.	Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning	arxiv
Liu et al.	Unlearning Concepts from Text-to-Video Diffusion Models	arxiv
Gandikota et al.	Erasing Conceptual Knowledge from Language Models	arxiv
Tu et al.	Towards Reliable Empirical Machine Unlearning Evaluation: A Cryptographic Game Perspective	arxiv
Zhuang et al.	UOE: Unlearning One Expert is Enough for Mixture-of-Experts LLMs	arxiv
Liu	Machine Unlearning in 2024	Blog Post

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
Kumar et al.	Privacy Adhering Machine Un-learning in NLP	ACL
Adolphs et al.	The CRINGE Loss: Learning what language not to model	ACL
Li et al.	Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data	ACL
Zhang et al.	Machine Unlearning Methodology base on Stochastic Teacher Network	ADMA
LeBlond et al.	Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile	AISec
Cong and Mahdavi	Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection	AISTATS
Wang et al.	BFU: Bayesian Federated Unlearning with Parameter Self-Sharing	Asia CCS
Lee and Woo	UNDO: Effective and Accurate Unlearning Method for Deep Neural Networks	CIKM
Ghazi et al.	Ticketed Learning-Unlearning Schemes	COLT
Chen et al.	Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary	CVPR
Schramowski et al.	Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models	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
Mireshghallah et al.	Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN	EMNLP
Kassem et al.	Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models	EMNLP
Wu et al.	DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models	DMNLP
Gandikota et al.	Erasing Concepts from Diffusion Models	ICCV
Kumari et al.	Ablating Concepts in Text-to-Image Diffusion Models	ICCV
Liu et al.	MUter: Machine Unlearning on Adversarially Trained Models	ICCV
Koh et al.	Disposable Transfer Learning for Selective Source Task Unlearning	ICCV
Dukler et al.	SAFE: Machine Unlearning With Shard Graphs	ICCV
Zheng et al.	Graph Unlearning Using Knowledge Distillation	ICICS
Cheng et al.	GNNDelete: A General Strategy for Unlearning in Graph Neural Networks	ICLR
Basu et al.	Localizing and Editing Knowledge In Text-to-Image Generative Models	ICLR
Chien et al.	Efficient Model Updates for Approximate Unlearning of Graph-Structured Data	ICLR
Ilharco et al.	Editing models with task arithmetic	ICLR
Che et al.	Fast Federated Machine Unlearning with Nonlinear Functional Theory	ICML
Krishna et al.	Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten	ICML
Liu et al.	Machine Unlearning with Affine Hyperplane Shifting and Maintaining for Image Classification	ICONIP
Xiong et al.	Exact-Fun: An Exact and Efficient Federated Unlearning Approach	IEEE ICDM
Su and Li	Asynchronous Federated Unlearning	IEEE INFOCOM
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
Ni et al.	Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion	ACM MM
Li et al.	Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems	MM
Hu et al.	A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services	NDSS
Warnecke et al.	Machine Unlearning for Features and Labels	NDSS
Brack et al.	SEGA: Instructing Text-to-Image Models using Semantic Guidance	NeurIPS
Chen et al.	Fast Model Debias with Machine Unlearning	NeurIPS
Kurmanji et al.	Towards Unbounded Machine Unlearning	NeurIPS
Li et al.	UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition	NeurIPS
Liu et al.	Certified Minimax Unlearning with Generalization Rates and Deletion Capacity	NeurIPS
Jia et al.	Model Sparsification Can Simplify Machine Unlearning	NeurIPS
Wei et al.	Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples	NeurIPS
Di et al.	Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks	NeurIPS
Heng et al.	Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models	NeurIPS
Wang et al.	Concept Algebra for (Score-Based) Text-Controlled Generative Models	NeurIPS
Zhao et al.	Static and Sequential Malicious Attacks in the Context of Selective Forgetting	NeurIPS
Belrose et al.	LEACE: Perfect linear concept erasure in closed form	NeurIPS
Zhang et al.	Composing Parameter-Efficient Modules with Arithmetic Operation	NeurIPS
Leysen	Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender Systems	RecSys
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
Kurmanji et al.	Machine Unlearning in Learned Databases: An Experimental Analysis	SIGMOD
Wu et al.	DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning	SIGMOD
Wang et al.	Inductive Graph Unlearning	USENIX Security
Xia et al.	Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets	VLDB
Sun et al.	Lazy Machine Unlearning Strategy for Random Forests	WISA
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
Ye and Lu	Sequence Unlearning for Sequential Recommender Systems	AI
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
Sai et al.	Machine Un-learning: An Overview of Techniques, Applications, and Future Directions	Cognitive Computation
Tang et al.	Ensuring User Privacy and Model Security via Machine Unlearning: A Review	Computers, Materials, and Continua
Deng et al.	Vertical Federated Unlearning on the Logistic Regression Model	Electronics
Zhou et al.	A unified method to revoke the private data of patients in intelligent healthcare with audit to forget	Europe PMC
Li et al.	Selective and Collaborative Influence Function for Efficient Recommendation Unlearning	Expert Systems with Applications
Zeng at al.	Towards Highly-efficient and Accurate Services QoS Prediction via Machine Unlearning	IEEE Access
Zhao et al.	Federated Unlearning With Momentum Degradation	IEEE IOT Journal
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
Wang et al.	Machine Unlearning via Representation Forgetting with Parameter Self-Sharing	IEEE Trans. Info. Forensics and Security
Guo et al.	Verifying in the Dark: Verifiable Machine Unlearning by Using Invisible Backdoor Triggers	IEEE Trans. Info. Forensics and Security
Zhang et al.	FedRecovery: Differentially Private Machine Unlearning for Federated Learning Frameworks	IEEE Trans. Info. Forensics and Security
Guo et al.	FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side	IEEE Trans. Network Science and Engineering
Tarun et al.	Fast Yet Effective Machine Unlearning	IEEE Trans. Neural Net. and Learn. Systems
Tang et al.	Fuzzy rough unlearning model for feature selection	International Journal of Approximate Reasoning
Zhu et al.	Hierarchical Machine Unlearning	Learning and Intelligent Optimization
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
Borkar et al.	What can we learn from Data Leakage and Unlearning for Law?	ICML Workshop
Kim et al.	Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models	ICML Workshop
Kadhe et al.	FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs	NeurIPS Workshop
Li et al.	Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data	TrustNLP Workshop
Abbasi et al.	CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation	arXiv
Cotogni et al.	DUCK: Distance-based Unlearning via Centroid Kinematics	arXiv
Dhasade et al.	QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation	arXiv
Huang et al.	Tight Bounds for Machine Unlearning via Differential Privacy	arXiv
Jin et al.	Forgettable Federated Linear Learning with Certified Data Removal	arXiv
Kodge et al.	Deep Unlearning: Fast and Efficient Training-free Approach to Controlled Forgetting	arXiv
Li and Ghosh	Random Relabeling for Efficient Machine Unlearning	arXiv
Li et al.	Subspace based Federated Unlearning	arXiv
Liu et al.	Recommendation Unlearning via Matrix Correction	arXiv
Qu et al.	Learn to Unlearn: A Survey on Machine Unlearning	arXiv
Ramachandra and Sethi	Machine Unlearning for Causal Inference	arXiv
Shah et al.	Unlearning via Sparse Representations	arXiv
Si et al.	Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges	arXiv
Sinha et al.	Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation	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
Patil et al.	Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks	arxiv
Jahanian et al.	Protecting the Neural Networks against FGSM Attack Using Machine Unlearning	Research Square
Dai et al.	Training Data Attribution for Diffusion Models	arxiv
Fan	Machine learning and unlearning for IoT anomaly detection	Thesis
Casper	Deep Forgetting & Unlearning for Safely-Scoped LLMs	Blog Post

2022

Author(s)	Title	Venue
Chundawat et al.	Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher	AAAI
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
Tarun et al.	Deep Regression Unlearning	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
Meng et al.	Locating and Editing Factual Associations in GPT	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
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
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
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
Said et al.	A Survey of Graph Unlearning	arXiv
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	Report
Cong and Mahdavi	GraphEditor: An Efficient Graph Representation Learning and Unlearning Approach	Report
Wu et al.	Provenance-based Model Maintenance: Implications for Privacy	Report

2021

Author(s)	Title	Venue
Graves et al.	Amnesiac Machine Learning	AAAI
Yu et al.	How Does Data Augmentation Affect Privacy in Machine Learning?	AAAI
Liu et al.	DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts	ACL
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
Dam et al.	Delete My Account: Impact of Data Deletion on Machine Learning Classifiers	ICSSA
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.	ModelPred: A Framework for Predicting Trained Model from Training Data	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
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
Chen et al.	A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine	Cluster Computing
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