Towards Natural Machine Unlearning
Fuente:
arXiv
Saved in:
| Main Authors: | He, Zhengbao, Li, Tao, Cheng, Xinwen, Huang, Zhehao, Huang, Xiaolin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
by: Huang, Zhehao, et al.
Published: (2024)
by: Huang, Zhehao, et al.
Published: (2024)
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
by: Cheng, Xinwen, et al.
Published: (2024)
by: Cheng, Xinwen, et al.
Published: (2024)
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
by: Huang, Zhehao, et al.
Published: (2025)
by: Huang, Zhehao, et al.
Published: (2025)
Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information
by: Cheng, Xinwen, et al.
Published: (2026)
by: Cheng, Xinwen, et al.
Published: (2026)
Friendly Sharpness-Aware Minimization
by: Li, Tao, et al.
Published: (2024)
by: Li, Tao, et al.
Published: (2024)
MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
by: Yang, Ruikai, et al.
Published: (2024)
by: Yang, Ruikai, et al.
Published: (2024)
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
by: Wu, Yingwen, et al.
Published: (2024)
by: Wu, Yingwen, et al.
Published: (2024)
Trainable Weight Averaging: Accelerating Training and Improving Generalization
by: Li, Tao, et al.
Published: (2022)
by: Li, Tao, et al.
Published: (2022)
RAIN-Merging: A Gradient-Free Method to Enhance Instruction Following in Large Reasoning Models with Preserved Thinking Format
by: Huang, Zhehao, et al.
Published: (2026)
by: Huang, Zhehao, et al.
Published: (2026)
Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape
by: Li, Tao, et al.
Published: (2024)
by: Li, Tao, et al.
Published: (2024)
T2I-ConBench: Text-to-Image Benchmark for Continual Post-training
by: Huang, Zhehao, et al.
Published: (2025)
by: Huang, Zhehao, et al.
Published: (2025)
Online Continual Learning via Logit Adjusted Softmax
by: Huang, Zhehao, et al.
Published: (2023)
by: Huang, Zhehao, et al.
Published: (2023)
Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
by: Liu, Yuhang, et al.
Published: (2025)
by: Liu, Yuhang, et al.
Published: (2025)
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)
by: Huang, Tao, et al.
Published: (2024)
Learning to Unlearn for Robust Machine Unlearning
by: Huang, Mark He, et al.
Published: (2024)
by: Huang, Mark He, et al.
Published: (2024)
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning
by: Wu, Tao, et al.
Published: (2024)
by: Wu, Tao, et al.
Published: (2024)
Towards Source-Free Machine Unlearning
by: Ahmed, Sk Miraj, et al.
Published: (2025)
by: Ahmed, Sk Miraj, et al.
Published: (2025)
Machine Unlearning under Retain-Forget Entanglement
by: Cheng, Jingpu, et al.
Published: (2026)
by: Cheng, Jingpu, et al.
Published: (2026)
VL-RouterBench: A Benchmark for Vision-Language Model Routing
by: Huang, Zhehao, et al.
Published: (2025)
by: Huang, Zhehao, et al.
Published: (2025)
Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)
by: Yuan, Xun, et al.
Published: (2025)
Towards Reliable Forgetting: A Survey on Machine Unlearning Verification
by: Xue, Lulu, et al.
Published: (2025)
by: Xue, Lulu, et al.
Published: (2025)
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)
by: Han, Ling, et al.
Published: (2024)
Towards Reliable Testing of Machine Unlearning
by: Mazhar, Anna, et al.
Published: (2026)
by: Mazhar, Anna, et al.
Published: (2026)
Towards Independence Criterion in Machine Unlearning of Features and Labels
by: Han, Ling, et al.
Published: (2024)
by: Han, Ling, et al.
Published: (2024)
Toward Reliable Machine Unlearning: Theory, Algorithms, and Evaluation
by: Ebrahimpour-Boroojeny, Ali
Published: (2025)
by: Ebrahimpour-Boroojeny, Ali
Published: (2025)
On the Impossibility of Retrain Equivalence in Machine Unlearning
by: Yu, Jiatong, et al.
Published: (2025)
by: Yu, Jiatong, et al.
Published: (2025)
FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
by: Li, Zitong, et al.
Published: (2025)
by: Li, Zitong, et al.
Published: (2025)
Soft Weighted Machine Unlearning
by: Qiao, Xinbao, et al.
Published: (2025)
by: Qiao, Xinbao, et al.
Published: (2025)
Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)
by: Chen, Taozhao, et al.
Published: (2025)
Towards Aligned Data Forgetting via Twin Machine Unlearning
by: Niu, Zhenxing, et al.
Published: (2025)
by: Niu, Zhenxing, et al.
Published: (2025)
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
by: Wang, Haoyu, et al.
Published: (2026)
by: Wang, Haoyu, et al.
Published: (2026)
Siamese Machine Unlearning with Knowledge Vaporization and Concentration
by: Xie, Songjie, et al.
Published: (2024)
by: Xie, Songjie, et al.
Published: (2024)
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2024)
by: Wang, Cheng-Long, et al.
Published: (2024)
Efficient Verified Machine Unlearning For Distillation
by: Quan, Yijun, et al.
Published: (2025)
by: Quan, Yijun, et al.
Published: (2025)
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
by: Chien, Eli, et al.
Published: (2024)
by: Chien, Eli, et al.
Published: (2024)
Debiasing Machine Unlearning with Counterfactual Examples
by: Chen, Ziheng, et al.
Published: (2024)
by: Chen, Ziheng, 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)
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
by: Shaik, Thanveer, et al.
Published: (2023)
by: Shaik, Thanveer, et al.
Published: (2023)
An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
by: Gao, Yichen, et al.
Published: (2026)
by: Gao, Yichen, et al.
Published: (2026)
Similar Items
-
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
by: Huang, Zhehao, et al.
Published: (2024) -
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
by: Cheng, Xinwen, et al.
Published: (2024) -
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
by: Huang, Zhehao, et al.
Published: (2025) -
Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information
by: Cheng, Xinwen, et al.
Published: (2026) -
Friendly Sharpness-Aware Minimization
by: Li, Tao, et al.
Published: (2024)