MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Haoyu, Huang, Zhuo, Wang, Xiaolong, Han, Bo, Lin, Zhiwei, Liu, Tongliang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
by: Yang, Puning, et al.
Published: (2025)
by: Yang, Puning, et al.
Published: (2025)
Understanding Robust Overfitting from the Feature Generalization Perspective
by: Yu, Chaojian, et al.
Published: (2023)
by: Yu, Chaojian, et al.
Published: (2023)
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
Decoupling the Class Label and the Target Concept in Machine Unlearning
by: Zhu, Jianing, et al.
Published: (2024)
by: Zhu, Jianing, et al.
Published: (2024)
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
by: Zuo, Zhiwei, et al.
Published: (2024)
by: Zuo, Zhiwei, et al.
Published: (2024)
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)
OpenGU: A Comprehensive Benchmark for Graph Unlearning
by: Fan, Bowen, et al.
Published: (2025)
by: Fan, Bowen, et al.
Published: (2025)
Classifying Long-tailed and Label-noise Data via Disentangling and Unlearning
by: Shu, Chen, et al.
Published: (2025)
by: Shu, Chen, et al.
Published: (2025)
FairGU: Fairness-aware Graph Unlearning in Social Networks
by: Luo, Renqiang, et al.
Published: (2026)
by: Luo, Renqiang, et al.
Published: (2026)
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
Reference-Guided Machine Unlearning
by: Mirlach, Jonas, et al.
Published: (2026)
by: Mirlach, Jonas, et al.
Published: (2026)
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
by: Lin, Runqi, et al.
Published: (2023)
by: Lin, Runqi, et al.
Published: (2023)
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
by: Chien, Eli, et al.
Published: (2024)
by: Chien, Eli, et al.
Published: (2024)
Few-Shot Adversarial Prompt Learning on Vision-Language Models
by: Zhou, Yiwei, et al.
Published: (2024)
by: Zhou, Yiwei, et al.
Published: (2024)
Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)
by: Chen, Taozhao, et al.
Published: (2025)
The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
by: Xiao, Yang, et al.
Published: (2025)
by: Xiao, Yang, et al.
Published: (2025)
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
by: Wang, Bin, et al.
Published: (2026)
by: Wang, Bin, et al.
Published: (2026)
Learning to Unlearn for Robust Machine Unlearning
by: Huang, Mark He, et al.
Published: (2024)
by: Huang, Mark He, et al.
Published: (2024)
Machine Unlearning in Low-Dimensional Feature Subspace
by: Fang, Kun, et al.
Published: (2026)
by: Fang, Kun, et al.
Published: (2026)
Bifrost: Steering Strategic Trajectories to Bridge Contextual Gaps for Self-Improving Agents
by: Tran, Quan M., et al.
Published: (2026)
by: Tran, Quan M., 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)
Label Smoothing Improves Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)
by: Di, Zonglin, et al.
Published: (2024)
Disentangle Sample Size and Initialization Effect on Perfect Generalization for Single-Neuron Target
by: Zhao, Jiajie, et al.
Published: (2024)
by: Zhao, Jiajie, et al.
Published: (2024)
Instance-dependent Early Stopping
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by: Lin, Runqi, et al.
Published: (2024)
by: Lin, Runqi, et al.
Published: (2024)
What If the Input is Expanded in OOD Detection?
by: Zhang, Boxuan, et al.
Published: (2024)
by: Zhang, Boxuan, et al.
Published: (2024)
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)
by: Han, Ling, et al.
Published: (2024)
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
Towards Natural Machine Unlearning
by: He, Zhengbao, et al.
Published: (2024)
by: He, Zhengbao, et al.
Published: (2024)
Efficient Knowledge Graph Unlearning with Zeroth-order Information
by: Xiao, Yang, et al.
Published: (2025)
by: Xiao, Yang, et al.
Published: (2025)
Mechanistic Independence: A Principle for Identifiable Disentangled Representations
by: Matthes, Stefan, et al.
Published: (2025)
by: Matthes, Stefan, et al.
Published: (2025)
Benchmarking Federated Machine Unlearning methods for Tabular Data
by: Xiao, Chenguang, et al.
Published: (2025)
by: Xiao, Chenguang, et al.
Published: (2025)
Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
by: Wang, Houzhe, et al.
Published: (2026)
by: Wang, Houzhe, et al.
Published: (2026)
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs
by: Wang, Yue, et al.
Published: (2025)
by: Wang, Yue, et al.
Published: (2025)
Revisiting Machine Unlearning with Dimensional Alignment
by: Seo, Seonguk, et al.
Published: (2024)
by: Seo, Seonguk, et al.
Published: (2024)
LLM Unlearning with LLM Beliefs
by: Li, Kemou, et al.
Published: (2025)
by: Li, Kemou, et al.
Published: (2025)
Distribution-Guided and Constrained Quantum Machine Unlearning
by: Malik, Nausherwan, et al.
Published: (2026)
by: Malik, Nausherwan, et al.
Published: (2026)
Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning
by: Sula, Nexhi, et al.
Published: (2024)
by: Sula, Nexhi, et al.
Published: (2024)
Targeted Unlearning with Single Layer Unlearning Gradient
by: Cai, Zikui, et al.
Published: (2024)
by: Cai, Zikui, et al.
Published: (2024)
Similar Items
-
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
by: Huang, Zhuo, et al.
Published: (2026) -
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning
by: Yang, Puning, et al.
Published: (2025) -
Understanding Robust Overfitting from the Feature Generalization Perspective
by: Yu, Chaojian, et al.
Published: (2023) -
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024) -
Decoupling the Class Label and the Target Concept in Machine Unlearning
by: Zhu, Jianing, et al.
Published: (2024)