| 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 |
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| Jaeung Lee |
Machine Unlearning Comparator |
Github |
| Liu |
Machine Unlearning in 2024 |
Blog Post |
