{"total":244,"rows":[{"id":"2209.02299","title":"A Survey of Machine Unlearning","created":"2022-09-06","cited_by_count":312,"area":"safety"},{"id":"2310.10683","title":"Large Language Model Unlearning","created":"2023-10-14","cited_by_count":302,"area":"safety"},{"id":"2108.11577","title":"Machine Unlearning of Features and Labels","created":"2021-08-26","cited_by_count":229,"area":"safety"},{"id":"2111.08947","title":"Fast Yet Effective Machine Unlearning","created":"2021-11-17","cited_by_count":217,"area":"safety"},{"id":"2304.04934","title":"Model Sparsity Can Simplify Machine Unlearning","created":"2023-04-11","cited_by_count":214,"area":"safety"},{"id":"2110.11891","title":"On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning","created":"2021-10-22","cited_by_count":171,"area":"safety"},{"id":"2104.03413","title":"Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective","created":"2021-04-07","cited_by_count":161,"area":"safety"},{"id":"2408.00761","title":"Tamper-Resistant Safeguards for Open-Weight LLMs","created":"2024-08-01","cited_by_count":138,"area":"safety"},{"id":"2203.07320","title":"The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining","created":"2022-03-14","cited_by_count":118,"area":"safety"},{"id":"2110.11794","title":"Federated Unlearning via Class-Discriminative Pruning","created":"2021-10-22","cited_by_count":115,"area":"safety"},{"id":"2404.18239","title":"SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning","created":"2024-04-28","cited_by_count":104,"area":"safety"},{"id":"2305.06535","title":"KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment","created":"2023-05-11","cited_by_count":99,"area":"safety"},{"id":"2205.01992","title":"Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning","created":"2022-05-04","cited_by_count":94,"area":"safety"},{"id":"2308.07061","title":"Machine Unlearning: Solutions and Challenges","created":"2023-08-14","cited_by_count":93,"area":"safety"},{"id":"2406.01257","title":"What makes unlearning hard and what to do about it","created":"2024-06-03","cited_by_count":89,"area":"safety"},{"id":"2310.12560","title":"Fast Model Debias with Machine Unlearning","created":"2023-10-19","cited_by_count":72,"area":"safety"},{"id":"2304.02835","title":"GIF: A General Graph Unlearning Strategy via Influence Function","created":"2023-04-06","cited_by_count":60,"area":"safety"},{"id":"2109.08266","title":"Hard to Forget: Poisoning Attacks on Certified Machine Unlearning","created":"2021-09-17","cited_by_count":52,"area":"safety"},{"id":"2401.06187","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","created":"2024-01-11","cited_by_count":52,"area":"safety"},{"id":"2406.09073","title":"Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition","created":"2024-06-13","cited_by_count":50,"area":"safety"},{"id":"2407.20516","title":"Machine Unlearning in Generative AI: A Survey","created":"2024-07-30","cited_by_count":49,"area":"safety"},{"id":"2205.12709","title":"VeriFi: Towards Verifiable Federated Unlearning","created":"2022-05-25","cited_by_count":46,"area":"safety"},{"id":"2409.19732","title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","created":"2024-09-29","cited_by_count":45,"area":"safety"},{"id":"2106.15093","title":"Certifiable Machine Unlearning for Linear Models","created":"2021-06-29","cited_by_count":42,"area":"safety"},{"id":"2110.06904","title":"Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks","created":"2021-10-13","cited_by_count":40,"area":"safety"},{"id":"2402.05813","title":"Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models","created":"2024-02-08","cited_by_count":39,"area":"safety"},{"id":"2312.04095","title":"Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection","created":"2023-12-07","cited_by_count":37,"area":"safety"},{"id":"2406.17216","title":"Machine Unlearning Fails to Remove Data Poisoning Attacks","created":"2024-06-25","cited_by_count":37,"area":"safety"},{"id":"2407.16997","title":"Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective","created":"2024-07-24","cited_by_count":36,"area":"safety"},{"id":"2405.07406","title":"Machine Unlearning: A Comprehensive Survey","created":"2024-05-13","cited_by_count":35,"area":"safety"},{"id":"2309.08230","title":"A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services","created":"2023-09-15","cited_by_count":34,"area":"safety"},{"id":"2106.01810","title":"Defending Against Backdoor Attacks in Natural Language Generation","created":"2021-06-03","cited_by_count":33,"area":"safety"},{"id":"2408.06223","title":"On Effects of Steering Latent Representation for Large Language Model Unlearning","created":"2024-08-12","cited_by_count":32,"area":"safety"},{"id":"2411.11315","title":"A Review on Machine Unlearning","created":"2024-11-18","cited_by_count":32,"area":"safety"},{"id":"2304.03093","title":"Inductive Graph Unlearning","created":"2023-04-06","cited_by_count":31,"area":"safety"},{"id":"2408.00920","title":"Towards Certified Unlearning for Deep Neural Networks","created":"2024-08-01","cited_by_count":31,"area":"safety"},{"id":"2203.11491","title":"Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation","created":"2022-03-22","cited_by_count":29,"area":"safety"},{"id":"2312.10336","title":"Certified Minimax Unlearning with Generalization Rates and Deletion Capacity","created":"2023-12-16","cited_by_count":29,"area":"safety"},{"id":"2401.11760","title":"Towards Effective and General Graph Unlearning via Mutual Evolution","created":"2024-01-22","cited_by_count":29,"area":"safety"},{"id":"2401.05146","title":"Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics","created":"2024-01-10","cited_by_count":27,"area":"safety"},{"id":"2406.16257","title":"Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning","created":"2024-06-24","cited_by_count":27,"area":"safety"},{"id":"2410.17509","title":"WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models","created":"2024-10-23","cited_by_count":26,"area":"safety"},{"id":"2202.13585","title":"Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten","created":"2022-02-28","cited_by_count":25,"area":"safety"},{"id":"2204.09092","title":"Indiscriminate Data Poisoning Attacks on Neural Networks","created":"2022-04-19","cited_by_count":25,"area":"safety"},{"id":"2210.09126","title":"Verifiable and Provably Secure Machine Unlearning","created":"2022-10-17","cited_by_count":25,"area":"safety"},{"id":"2405.20272","title":"Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable","created":"2024-05-30","cited_by_count":25,"area":"safety"},{"id":"2204.05986","title":"Machine Learning Security against Data Poisoning: Are We There Yet?","created":"2022-04-12","cited_by_count":23,"area":"safety"},{"id":"2303.03592","title":"Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks","created":"2023-03-07","cited_by_count":23,"area":"safety"},{"id":"2310.18574","title":"Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning","created":"2023-10-28","cited_by_count":22,"area":"safety"},{"id":"2406.09408","title":"Data Attribution for Text-to-Image Models by Unlearning Synthesized Images","created":"2024-06-13","cited_by_count":22,"area":"safety"},{"id":"2203.12964","title":"Knowledge Removal in Sampling-based Bayesian Inference","created":"2022-03-24","cited_by_count":21,"area":"safety"},{"id":"2311.09763","title":"Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations","created":"2023-11-16","cited_by_count":21,"area":"safety"},{"id":"2404.13588","title":"Machine Unlearning via Null Space Calibration","created":"2024-04-21","cited_by_count":21,"area":"safety"},{"id":"2408.00929","title":"Verification of Machine Unlearning is Fragile","created":"2024-08-01","cited_by_count":21,"area":"safety"},{"id":"2205.13680","title":"Membership Inference Attack Using Self Influence Functions","created":"2022-05-26","cited_by_count":20,"area":"safety"},{"id":"2502.12176","title":"Ten Challenging Problems in Federated Foundation Models","created":"2025-02-14","cited_by_count":20,"area":"safety"},{"id":"2503.22759","title":"Data Poisoning in Deep Learning: A Survey","created":"2025-03-27","cited_by_count":20,"area":"safety"},{"id":"2105.06209","title":"DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks","created":"2021-05-13","cited_by_count":19,"area":"safety"},{"id":"2306.03558","title":"Machine Unlearning: A Survey","created":"2023-06-06","cited_by_count":19,"area":"safety"},{"id":"2503.06339","title":"Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning","created":"2025-03-08","cited_by_count":19,"area":"safety"},{"id":"2401.15241","title":"Unlearning Traces the Influential Training Data of Language Models","created":"2024-01-26","cited_by_count":18,"area":"safety"},{"id":"2410.23232","title":"Attribute-to-Delete: Machine Unlearning via Datamodel Matching","created":"2024-10-30","cited_by_count":18,"area":"safety"},{"id":"2210.06516","title":"How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?","created":"2022-10-12","cited_by_count":17,"area":"safety"},{"id":"2403.09914","title":"ProMark: Proactive Diffusion Watermarking for Causal Attribution","created":"2024-03-14","cited_by_count":17,"area":"safety"},{"id":"2411.15537","title":"MUNBa: Machine Unlearning via Nash Bargaining","created":"2024-11-23","cited_by_count":17,"area":"safety"},{"id":"2510.19422","title":"LLM Unlearning with LLM Beliefs","created":"2025-10-22","cited_by_count":17,"area":"safety"},{"id":"2111.03120","title":"Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods","created":"2021-11-04","cited_by_count":16,"area":"safety"},{"id":"2403.03536","title":"Towards Efficient and Effective Unlearning of Large Language Models for Recommendation","created":"2024-03-06","cited_by_count":16,"area":"safety"},{"id":"2406.07687","title":"Adversarial Machine Unlearning","created":"2024-06-11","cited_by_count":16,"area":"safety"},{"id":"2310.05847","title":"Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems","created":"2023-10-06","cited_by_count":15,"area":"safety"},{"id":"2406.06186","title":"A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks","created":"2024-06-10","cited_by_count":15,"area":"safety"},{"id":"2307.10163","title":"Rethinking Backdoor Attacks","created":"2023-07-19","cited_by_count":14,"area":"safety"},{"id":"2408.09212","title":"Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier","created":"2024-08-17","cited_by_count":14,"area":"safety"},{"id":"2209.00939","title":"An Introduction to Machine Unlearning","created":"2022-09-02","cited_by_count":13,"area":"safety"},{"id":"2402.15109","title":"Remaining-data-free Machine Unlearning by Suppressing Sample Contribution","created":"2024-02-23","cited_by_count":13,"area":"safety"},{"id":"2303.14325","title":"Backdoor Attacks with Input-unique Triggers in NLP","created":"2023-03-25","cited_by_count":12,"area":"safety"},{"id":"2307.04550","title":"Gradient Surgery for One-shot Unlearning on Generative Model","created":"2023-07-10","cited_by_count":12,"area":"safety"},{"id":"2310.02164","title":"A Survey of Graph Unlearning","created":"2023-08-23","cited_by_count":12,"area":"safety"},{"id":"2406.08288","title":"Decoupling the Class Label and the Target Concept in Machine Unlearning","created":"2024-06-12","cited_by_count":12,"area":"safety"},{"id":"2503.13917","title":"Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels","created":"2025-03-18","cited_by_count":12,"area":"safety"},{"id":"2504.10185","title":"LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks","created":"2025-04-14","cited_by_count":12,"area":"safety"},{"id":"2309.14054","title":"Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks","created":"2023-09-25","cited_by_count":11,"area":"safety"},{"id":"2405.15495","title":"Towards Natural Machine Unlearning","created":"2024-05-24","cited_by_count":11,"area":"safety"},{"id":"2506.06278","title":"Distillation Robustifies Unlearning","created":"2025-06-06","cited_by_count":11,"area":"safety"},{"id":"2404.01206","title":"Machine Unlearning for Traditional Models and Large Language Models: A Short Survey","created":"2024-04-01","cited_by_count":10,"area":"safety"},{"id":"2406.11544","title":"Do Parameters Reveal More than Loss for Membership Inference?","created":"2024-06-17","cited_by_count":10,"area":"safety"},{"id":"2407.17710","title":"Revisiting Machine Unlearning with Dimensional Alignment","created":"2024-07-25","cited_by_count":10,"area":"safety"},{"id":"2506.05451","title":"Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety","created":"2025-06-05","cited_by_count":10,"area":"safety"},{"id":"2506.10424","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","created":"2025-06-12","cited_by_count":10,"area":"safety"},{"id":"2507.05578","title":"The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation","created":"2025-07-08","cited_by_count":10,"area":"safety"},{"id":"2103.12399","title":"The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?","created":"2021-03-23","cited_by_count":9,"area":"safety"},{"id":"2406.07698","title":"Label Smoothing Improves Machine Unlearning","created":"2024-06-11","cited_by_count":9,"area":"safety"},{"id":"2410.01276","title":"Deep Unlearn: Benchmarking Machine Unlearning for Image Classification","created":"2024-10-02","cited_by_count":9,"area":"safety"},{"id":"2410.05583","title":"NegMerge: Sign-Consensual Weight Merging for Machine Unlearning","created":"2024-10-08","cited_by_count":9,"area":"safety"},{"id":"2501.19202","title":"Improving LLM Unlearning Robustness via Random Perturbations","created":"2025-01-31","cited_by_count":9,"area":"safety"},{"id":"2506.06486","title":"A Certified Unlearning Approach without Access to Source Data","created":"2025-06-06","cited_by_count":9,"area":"safety"},{"id":"2310.05331","title":"Unlearning with Fisher Masking","created":"2023-10-09","cited_by_count":8,"area":"safety"},{"id":"2402.12626","title":"Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors","created":"2024-02-20","cited_by_count":8,"area":"safety"},{"id":"2311.10448","title":"DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal","created":"2023-11-17","cited_by_count":7,"area":"safety"},{"id":"2503.02312","title":"Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization","created":"2025-03-04","cited_by_count":7,"area":"safety"}],"next_cursor":"100"}