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| Liu et al. | Backdoor Attacks via Machine Unlearning | AAAI |
| You et al. | RRL: Recommendation Reverse Learning | AAAI |
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| Chen et al. | WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models | ECAI |
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| 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 |
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| Liu et al. | Tangent Transformers for Composition, Privacy and Removal | ICLR |
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| Shen et al. | Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models | ICLR |
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| Wang et al. | A Unified and General Framework for Continual Learning | ICLR |
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| Wang et al. | LLM Unlearning via Loss Adjustment with Only Forget Data | ICLR |
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| Li et al. | The WMDP benchmark: measuring and reducing malicious use with unlearning | ICML |
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| Zhang et al. | SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning | ICPR |
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| 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 |
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| 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 |
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| Wu et al. | Cross-model Control: Improving Multiple Large Language Models in One-time Training | NeurIPS |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| Tamirisa et al. | Tamper-Resistant Safeguards for Open-Weight LLMs | arxiv |
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| Lu et al. | Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation | arxiv |
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| 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 |
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| Lu et al. | Learn and Unlearn in Multilingual LLMs | arxiv |
| Ma et al. | Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset | arxiv |
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| 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 |
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| Liu | Machine Unlearning in 2024 | Blog Post |
