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awesome-machine-unlearning
Awesome Machine Unlearning (A Survey of Machine Unlearning)
https://github.com/tamlhp/awesome-machine-unlearning
- SoK: Challenges and Opportunities in Federated Unlearning
- Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics
- Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
- Rethinking Machine Unlearning for Large Language Models
- Threats, Attacks, and Defenses in Machine Unlearning: A Survey
- A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
- A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions
- Machine Unlearning: Solutions and Challenges
- Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
- Machine Unlearning: A Survey
- An Introduction to Machine Unlearning
- Machine Unlearning: Its Need and Implementation Strategies
- Making machine learning forget
- “Amnesia” - A Selection of Machine Learning Models That Can Forget User Data Very Fast
- Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten
- Algorithms that remember: model inversion attacks and data protection law
- On the Trade-Off between Actionable Explanations and the Right to be Forgotten - | - |
- Post-Training Attribute Unlearning in Recommender Systems - | - | PoT-AU |
- Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems - | - | PoT-AU |
- CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation - | |
- Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective - | |
- Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning - | |
- ∇τ: Gradient-based and Task-Agnostic machine Unlearning - | - | |
- Towards Independence Criterion in Machine Unlearning of Features and Labels - | - | |
- Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning - | [[Code]](https://github.com/OPTML-Group/Unlearn-WorstCase) | |
- Corrective Machine Unlearning - | [[Code]](https://github.com/drimpossible/corrective-unlearning-bench) | |
- Fair Machine Unlearning: Data Removal while Mitigating Disparities - GROUP/fair-unlearning) | |
- Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models - Agnostic Forgetting | [[Code]](https://github.com/ShaofeiShen768/LAF) | |
- CaMU: Disentangling Causal Effects in Deep Model Unlearning
- SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation - Group/Unlearn-Saliency) | Weight Saliency |
- Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening - loops/selective-synaptic-dampening) | Retraining-free |
- Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers - wise unlearning | - | |
- Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening - loops/adaptive-selective-synaptic-dampening) | |
- Zero-Shot Machine Unlearning at Scale via Lipschitz Regularization - Unlearning-At-Scale) | Zero-shot |
- Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation - | |
- Towards bridging the gaps between the right to explanation and the right to be forgotten - | - | e |
- Unlearn What You Want to Forget: Efficient Unlearning for LLMs - NLP/Efficient_Unlearning/) | |
- Fast Model DeBias with Machine Unlearning - debias/) | |
- DUCK: Distance-based Unlearning via Centroid Kinematics
- Open Knowledge Base Canonicalization with Multi-task Unlearning - | |
- Unlearning via Sparse Representations - | Zero-shot Unlearning |
- SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning - | Vertical Federated Learning |
- Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks - | - | Camouflaged data poisoning attacks |
- Model Sparsity Can Simplify Machine Unlearning - sparse | [[Code]](https://github.com/OPTML-Group/Unlearn-Sparse) | Weight Pruning |
- Fast Model Debias with Machine Unlearning - | - | |
- FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning - | Federated Learning |
- Tight Bounds for Machine Unlearning via Differential Privacy - | - | |
- Machine Unlearning Methodology base on Stochastic Teacher Network - | - |
- Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening - loops/selective-synaptic-dampening) | |
- From Adaptive Query Release to Machine Unlearning - | - | Exact Unlearning |
- Towards Adversarial Evaluations for Inexact Machine Unlearning - k, CF-k | [[Code]](https://github.com/shash42/Evaluating-Inexact-Unlearning) |
- KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment - WANG/KGAUnlearn) | |
- On the Trade-Off between Actionable Explanations and the Right to be Forgotten - | - | |
- Towards Unbounded Machine Unlearning
- Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations - ALS | - | Exact Unlearning |
- To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods - | [[Code]](https://github.com/cleverhans-lab/machine-unlearning) | |
- Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization - | |
- Certified Data Removal in Sum-Product Networks
- Learning with Recoverable Forgetting - | |
- Continual Learning and Private Unlearning - XIX/Continual-Learning-Private-Unlearning) | |
- Verifiable and Provably Secure Machine Unlearning - | [[Code]](https://github.com/cleverhans-lab/verifiable-unlearning) | Certified Removal Mechanisms |
- VeriFi: Towards Verifiable Federated Unlearning - | Certified Removal Mechanisms |
- FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information - | recovery method |
- Fast Yet Effective Machine Unlearning - | |
- Membership Inference via Backdooring - inference-via-backdooring) | Membership Inferencing |
- Forget Unlearning: Towards True Data-Deletion in Machine Learning - | - | noisy gradient descent |
- Zero-Shot Machine Unlearning - | - | |
- Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations - | |
- Few-Shot Unlearning - | - | |
- Federated Unlearning: How to Efficiently Erase a Client in FL? - | - | federated learning |
- Machine Unlearning Method Based On Projection Residual - | - | Projection Residual Method |
- Hard to Forget: Poisoning Attacks on Certified Machine Unlearning - | [[Code]](https://github.com/ngmarchant/attack-unlearning) | Certified Removal Mechanisms |
- Athena: Probabilistic Verification of Machine Unlearning - | |
- FP2-MIA: A Membership Inference Attack Free of Posterior Probability in Machine Unlearning - MIA | - | inference attack |
- Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning - | - | |
- Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization - | Certified Removal Mechanisms |
- The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining - | [[Code]](https://github.com/yiliucs/federated-unlearning) | |
- Backdoor Defense with Machine Unlearning - | Backdoor defense |
- Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten - | MCMC Unlearning |
- Federated Unlearning for On-Device Recommendation - | - | |
- Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher - | - | Knowledge Adaptation |
- Efficient Two-Stage Model Retraining for Machine Unlearning - | - | |
- Learn to Forget: Machine Unlearning Via Neuron Masking - | Mask Gradients |
- Adaptive Machine Unlearning - | [[Code]](https://github.com/ChrisWaites/adaptive-machine-unlearning) | Differential Privacy |
- Descent-to-Delete: Gradient-Based Methods for Machine Unlearning - | - | Certified Removal Mechanisms |
- Remember What You Want to Forget: Algorithms for Machine Unlearning - | - | |
- FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models - | |
- Federated Unlearning - Code.zip?dl=0) | |
- Machine Unlearning via Algorithmic Stability - | Certified Removal Mechanisms |
- EMA: Auditing Data Removal from Trained Models
- Knowledge-Adaptation Priors - prior | [[Code]](https://github.com/team-approx-bayes/kpriors) | Knowledge Adaptation |
- PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models - | Knowledge Adaptation |
- Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks - | - | Certified Removal Mechanisms |
- Learn to Forget: User-Level Memorization Elimination in Federated Learning - | |
- Certified Data Removal from Machine Learning Models - | - | Certified Removal Mechanisms |
- Class Clown: Data Redaction in Machine Unlearning at Enterprise Scale - | - | Decremental Learning |
- A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine - | - | Decremental Learning |
- Making AI Forget You: Data Deletion in Machine Learning - | - | Decremental Learning |
- Lifelong Anomaly Detection Through Unlearning - | - | |
- Learning Not to Learn: Training Deep Neural Networks With Biased Data - | - | |
- Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning
- Understanding Black-box Predictions via Influence Functions - | [[Code]](https://github.com/kohpangwei/influence-release) | Certified Removal Mechanisms |
- Towards Making Systems Forget with Machine Unlearning - | |
- Towards Making Systems Forget with Machine Unlearning - | - | Statistical Query Learning |
- Incremental and decremental training for linear classification - | [[Code]](https://www.csie.ntu.edu.tw/~cjlin/papers/ws/) | Decremental Learning |
- Multiple Incremental Decremental Learning of Support Vector Machines - | - | Decremental Learning |
- Incremental and Decremental Learning for Linear Support Vector Machines - | - | Decremental Learning |
- Decremental Learning Algorithms for Nonlinear Langrangian and Least Squares Support Vector Machines - | - | Decremental Learning |
- Multicategory Incremental Proximal Support Vector Classifiers - | - | Decremental Learning |
- Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients - | - | Decremental Learning |
- Incremental and Decremental Support Vector Machine Learning - | - | Decremental Learning |
- Machine Unlearning for Image-to-Image Generative Models - | [[Code]](https://github.com/jpmorganchase/l2l-generator-unlearning) | Generative Models |
- Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
- Dissecting Language Models: Machine Unlearning via Selective Pruning - | LLM |
- Decentralized Federated Unlearning on Blockchain - | Directed Acyclic Graph |
- Unlink to Unlearn: Simplifying Edge Unlearning in GNNs - to-Unlearn) | GNN-based models |
- Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models - | LLM |
- Towards Effective and General Graph Unlearning via Mutual Evolution - | GNN-based models |
- Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation - Sub | [[Code]](https://github.com/HITsz-TMG/Ext-Sub) | LLM |
- FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models - unlearninggan) | GAN |
- Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models - NTK | - | CNN model |
- Certified Minimax Unlearning with Generalization Rates and Deletion Capacity - | - | Minimax model |
- FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs - | LLMs |
- Multimodal Machine Unlearning - | Multimodal Models |
- Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks - then-Unlearn | - | GAN |
- SAFE: Machine Unlearning With Shard Graphs - | GNN-based Models |
- MUter: Machine Unlearning on Adversarially Trained Models - | Adversarial Training Models |
- Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning - websoft/FedLU/) | GNN-based Models |
- One-Shot Machine Unlearning with Mnemonic Code - Shot MU | - | |
- Inductive Graph Unlearning - based Models |
- ERM-KTP: Knowledge-level Machine Unlearning via Knowledge Transfer - KTP | [[Code]](https://github.com/RUIYUN-ML/ERM-KTP) | |
- GNNDelete: A General Strategy for Unlearning in Graph Neural Networks - harvard/GNNDelete) | |
- Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search - LSH | [[Code]](https://anonymous.4open.science/r/ann-benchmarks-3786/README.md) | |
- Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection - based Models |
- Unrolling SGD: Understanding Factors Influencing Machine Unlearning - | [[Code]](https://github.com/cleverhans-lab/unrolling-sgd) | SGD |
- Graph Unlearning - Unlearning) | Graph Neural Networks |
- Certified Graph Unlearning - | [[Code]](https://github.com/thupchnsky/sgc_unlearn) | Graph Neural Networks |
- Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification - Abarghouei | _ICML_ | - | [[Code]](https://github.com/pbevan1/Skin-Deep-Unlearning) | CNN Models |
- Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning - | - | Bayesian Models |
- Unlearning Protected User Attributes in Recommendations with Adversarial Training - MULTVAE | [[Code]](https://github.com/CPJKU/adv-multvae) | Autoencoder-based Model |
- Recommendation Unlearning - Unlearning) | Attention-based Model |
- Knowledge Neurons in Pretrained Transformers - | [[Code]](https://github.com/Hunter-DDM/knowledge-neurons) | Transformers
- Memory-Based Model Editing at Scale - editing) | DNN-based Models |
- Forgetting Fast in Recommender Systems - | recommendation system |
- Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime - | - | GNN-based Models |
- Deep Regression Unlearning - | Regression Model |
- Quark: Controllable Text Generation with Reinforced Unlearning
- Forget-SVGD: Particle-Based Bayesian Federated Unlearning - SVGD | - | Bayesian Models |
- Machine Unlearning of Federated Clusters - | Federated clustering |
- Machine Unlearning for Image Retrieval: A Generative Scrubbing Approach - | - | DNN-based Models |
- Machine Unlearning: Linear Filtration for Logit-based Classifiers - | Softmax classifiers |
- Deep Unlearning via Randomized Conditionally Independent Hessians - CODEC | [[Code]](https://github.com/vsingh-group/LCODEC-deep-unlearning) | DNN-based Models |
- Challenges and Pitfalls of Bayesian Unlearning - | - | Bayesian Models |
- Federated Unlearning via Class-Discriminative Pruning - | - | CNN-Based |
- Active forgetting via influence estimation for neural networks - | Neural Network |
- Variational Bayesian unlearning - | Bayesian Models |
- Revisiting Machine Learning Training Process for Enhanced Data Privacy - | - | DNN-based Models |
- Knowledge Removal in Sampling-based Bayesian Inference - | [[Code]](https://github.com/fshp971/mcmc-unlearning) | Bayesian Models |
- Mixed-Privacy Forgetting in Deep Networks - | - | DNN-based Models |
- HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning - based Models |
- A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization - Bayesian| - | Bayesian Models |
- DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks - | DNN-based Models |
- Approximate Data Deletion from Machine Learning Models: Algorithms and Evaluations
- Bayesian Inference Forgetting
- Approximate Data Deletion from Machine Learning Models
- Online Forgetting Process for Linear Regression Models - OLS | - | Linear Models |
- RevFRF: Enabling Cross-domain Random Forest Training with Revocable Federated Learning - | Random Forrests |
- Coded Machine Unlearning - | - | Deep Learning Models |
- Machine Unlearning for Random Forests - | Random Forrest |
- Bayesian Variational Federated Learning and Unlearning in Decentralized Networks - | - | Bayesian Models |
- Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations - | - | DNN-based Models |
- Influence Functions in Deep Learning Are Fragile - | - | DNN-based Models |
- Deep Autoencoding Topic Model With Scalable Hybrid Bayesian Inference - | Bayesian Models |
- Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks - | - | DNN-based Models |
- Uncertainty in Neural Networks: Approximately Bayesian Ensembling - | [[Code]](https://teapearce.github.io/portfolio/github_io_1_ens/) | Bayesian Models |
- Certified Data Removal from Machine Learning Models - | - | DNN-based Models |
- DeltaGrad: Rapid retraining of machine learning models - based Models |
- Making AI Forget You: Data Deletion in Machine Learning - | - | Linear Models |
- “Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast - | [[Code]](https://github.com/schelterlabs/projects-amnesia) | Collaborative Filtering |
- A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine - | - | SVM |
- Neural Text Degeneration With Unlikelihood Training - based |
- Bayesian Neural Networks with Weight Sharing Using Dirichlet Processes - bnn) | Bayesian Models |
- Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems
- Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks - TSRML_ | - | [[Code]](https://github.com/Jimmy-di/camouflage-poisoning) | Data Poisoning |
- Forget Unlearning: Towards True Data Deletion in Machine Learning - | - | Data Influence |
- ARCANE: An Efficient Architecture for Exact Machine Unlearning - | Data Partition |
- PUMA: Performance Unchanged Model Augmentation for Training Data Removal - | Data Influence |
- Certifiable Unlearning Pipelines for Logistic Regression: An Experimental Study - | [[Code]](https://version.helsinki.fi/mahadeva/unlearning-experiments) | Data Influence |
- Zero-Shot Machine Unlearning - | - | Data Influence |
- GRAPHEDITOR: An Efficient Graph Representation Learning and Unlearning Approach - | GRAPHEDITOR | [[Code]](https://anonymous.4open.science/r/GraphEditor-NeurIPS22-856E/README.md) | Data Influence |
- Fast Model Update for IoT Traffic Anomaly Detection with Machine Unlearning - J_ | ViFLa | - | Data Partition |
- Learning to Refit for Convex Learning Problems - | Data Influence |
- Fast Yet Effective Machine Unlearning - | - | Data Augmentation |
- Learning with Selective Forgetting - | - | Data Augmentation |
- SSSE: Efficiently Erasing Samples from Trained Machine Learning Models - PRIML_ | SSSE | - | Data Influence |
- How Does Data Augmentation Affect Privacy in Machine Learning? - | [[Code]](https://github.com/dayu11/MI_with_DA) | Data Augmentation |
- Coded Machine Unlearning - | - | Data Partitioning |
- Machine Unlearning - lab/machine-unlearning) | Data Partitioning |
- How Does Data Augmentation Affect Privacy in Machine Learning? - | [[Code]](https://github.com/dayu11/MI_with_DA) | Data Augmentation |
- Amnesiac Machine Learning
- Unlearnable Examples: Making Personal Data Unexploitable - | [[Code]](https://github.com/HanxunH/Unlearnable-Examples) | Data Augmentation |
- Descent-to-Delete: Gradient-Based Methods for Machine Unlearning - | - | Data Influence |
- Fawkes: Protecting Privacy against Unauthorized Deep Learning Models - Shan/fawkes) | Data Augmentation |
- PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models - opt | - | Data Influence |
- DeltaGrad: Rapid retraining of machine learning models
- UnlearnCanvas
- MUFAC & MUCAC
- MNIST
- CIFAR
- SVHN
- LSUN
- ImageNet
- Adult
- Breast Cancer
- Diabetes
- IMDB Review
- Reuters
- Newsgroup
- Epileptic Seizure
- Activity Recognition
- Botnet
- OGB
- Cora
- MovieLens
- 
Programming Languages