Distributional Machine Unlearning via Selective Data Removal
Fuente:
arXiv
Saved in:
| Main Authors: | Allouah, Youssef, Guerraoui, Rachid, Koyejo, Sanmi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025)
by: Koloskova, Anastasia, et al.
Published: (2025)
Balancing Privacy, Robustness, and Efficiency in Machine Learning
by: Allouah, Youssef, et al.
Published: (2023)
by: Allouah, Youssef, et al.
Published: (2023)
Towards Trustworthy Federated Learning with Untrusted Participants
by: Allouah, Youssef, et al.
Published: (2025)
by: Allouah, Youssef, et al.
Published: (2025)
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
The Privacy Power of Correlated Noise in Decentralized Learning
by: Allouah, Youssef, et al.
Published: (2024)
by: Allouah, Youssef, et al.
Published: (2024)
Invariant Aggregator for Defending against Federated Backdoor Attacks
by: Wang, Xiaoyang, et al.
Published: (2022)
by: Wang, Xiaoyang, et al.
Published: (2022)
Machine Unlearning Fails to Remove Data Poisoning Attacks
by: Pawelczyk, Martin, et al.
Published: (2024)
by: Pawelczyk, Martin, et al.
Published: (2024)
A Robust Certified Machine Unlearning Method Under Distribution Shift
by: Guo, Jinduo, et al.
Published: (2026)
by: Guo, Jinduo, et al.
Published: (2026)
Adversarial Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)
by: Di, Zonglin, et al.
Published: (2024)
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)
by: Lee, Hong kyu, et al.
Published: (2024)
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
by: Chien, Eli, et al.
Published: (2024)
by: Chien, Eli, et al.
Published: (2024)
Verification of Machine Unlearning is Fragile
by: Zhang, Binchi, et al.
Published: (2024)
by: Zhang, Binchi, et al.
Published: (2024)
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
by: Brodzinski, Carl E. J.
Published: (2024)
by: Brodzinski, Carl E. J.
Published: (2024)
Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)
by: Yuan, Xun, et al.
Published: (2025)
Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)
by: Dilworth, Robert
Published: (2025)
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
by: Inane, Ahmed Mehdi, et al.
Published: (2026)
by: Inane, Ahmed Mehdi, et al.
Published: (2026)
Data Unlearning Beyond Uniform Forgetting via Diffusion Time and Frequency Selection
by: Park, Jinseong, et al.
Published: (2025)
by: Park, Jinseong, et al.
Published: (2025)
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
by: Zhou, Xinjie, et al.
Published: (2026)
by: Zhou, Xinjie, et al.
Published: (2026)
SoK: Machine Unlearning for Large Language Models
by: Ren, Jie, et al.
Published: (2025)
by: Ren, Jie, et al.
Published: (2025)
TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
by: Wang, Weiqi, et al.
Published: (2025)
by: Wang, Weiqi, et al.
Published: (2025)
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
by: Bertran, Martin, et al.
Published: (2024)
by: Bertran, Martin, et al.
Published: (2024)
MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
Forgetting Similar Samples: Can Machine Unlearning Do it Better?
by: Xu, Heng, et al.
Published: (2026)
by: Xu, Heng, et al.
Published: (2026)
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
by: Chen, Aobo, et al.
Published: (2026)
by: Chen, Aobo, et al.
Published: (2026)
TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
by: Zhou, Chunyi, et al.
Published: (2024)
by: Zhou, Chunyi, et al.
Published: (2024)
BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
by: Lu, Bingguang, et al.
Published: (2025)
by: Lu, Bingguang, et al.
Published: (2025)
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
by: Xu, Heng, et al.
Published: (2024)
by: Xu, Heng, et al.
Published: (2024)
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Attesting Distributional Properties of Training Data for Machine Learning
by: Duddu, Vasisht, et al.
Published: (2023)
by: Duddu, Vasisht, et al.
Published: (2023)
SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
by: Wang, Weiqi, et al.
Published: (2025)
by: Wang, Weiqi, et al.
Published: (2025)
Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
by: Zheng, Weidong, et al.
Published: (2026)
by: Zheng, Weidong, et al.
Published: (2026)
Machine Unlearning for Traditional Models and Large Language Models: A Short Survey
by: Xu, Yi
Published: (2024)
by: Xu, Yi
Published: (2024)
No, of Course I Can! Deeper Fine-Tuning Attacks That Bypass Token-Level Safety Mechanisms
by: Kazdan, Joshua, et al.
Published: (2025)
by: Kazdan, Joshua, et al.
Published: (2025)
A Certified Unlearning Approach without Access to Source Data
by: Basaran, Umit Yigit, et al.
Published: (2025)
by: Basaran, Umit Yigit, et al.
Published: (2025)
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
by: Naderloui, Nima, et al.
Published: (2025)
by: Naderloui, Nima, et al.
Published: (2025)
Reinforcement Unlearning
by: Ye, Dayong, et al.
Published: (2023)
by: Ye, Dayong, et al.
Published: (2023)
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)
by: Liu, Zheyuan, et al.
Published: (2023)
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)
by: Zhang, Jiahao, et al.
Published: (2026)
Verifiable Unlearning on Edge
by: Maheri, Mohammad M, et al.
Published: (2025)
by: Maheri, Mohammad M, et al.
Published: (2025)
Similar Items
-
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
by: Allouah, Youssef, et al.
Published: (2024) -
Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025) -
Balancing Privacy, Robustness, and Efficiency in Machine Learning
by: Allouah, Youssef, et al.
Published: (2023) -
Towards Trustworthy Federated Learning with Untrusted Participants
by: Allouah, Youssef, et al.
Published: (2025) -
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)