Cover Image

LoReUn: Data Itself Implicitly Provides Cues to Improve Machine Unlearning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Xiang, Shen, Qianli, Wang, Haonan, Kawaguchi, Kenji
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization
by: Shen, Qianli, et al.
Published: (2024)

Data Augmentation Improves Machine Unlearning
by: Falcao, Andreza M. C., et al.
Published: (2025)

Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
by: Zhou, Shiji, et al.
Published: (2025)

The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline
by: Wang, Haonan, et al.
Published: (2024)

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
by: Cao, Xuemei, et al.
Published: (2025)

Re-understanding Graph Unlearning through Memorization
by: Ding, Pengfei, et al.
Published: (2026)

Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
by: Rahulamathavan, Yogachandran, et al.
Published: (2026)

VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models
by: Li, Xiang, et al.
Published: (2023)

Agentic Unlearning: When LLM Agent Meets Machine Unlearning
by: Wang, Bin, et al.
Published: (2026)

Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
by: Ma, Le, et al.
Published: (2025)

Machine Unlearning in Contrastive Learning
by: Wang, Zixin, et al.
Published: (2024)

Towards Aligned Data Removal via Twin Machine Unlearning
by: Ji, Haoxuan, et al.
Published: (2024)

Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data
by: Gong, Chen, et al.
Published: (2025)

Machine Unlearning: Solutions and Challenges
by: Xu, Jie, et al.
Published: (2023)

LoReC: Rethinking Large Language Models for Graph Data Analysis
by: Zhan, Hongyu, et al.
Published: (2026)

Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
by: Cadet, Xavier F., et al.
Published: (2024)

Zero-Shot Machine Unlearning with Proxy Adversarial Data Generation
by: Chen, Huiqiang, et al.
Published: (2025)

Fair Machine Unlearning: Data Removal while Mitigating Disparities
by: Oesterling, Alex, et al.
Published: (2023)

Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
by: Han, Ling, et al.
Published: (2024)

Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning
by: Zaman, Md Mahabub Uz, et al.
Published: (2025)

Beyond Superficial Forgetting: Thorough Unlearning through Knowledge Density Estimation and Block Re-insertion
by: Guo, Feng, et al.
Published: (2025)

Debiasing Machine Unlearning with Counterfactual Examples
by: Chen, Ziheng, et al.
Published: (2024)

Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)

Group-robust Machine Unlearning
by: De Min, Thomas, et al.
Published: (2025)

Soft Weighted Machine Unlearning
by: Qiao, Xinbao, et al.
Published: (2025)

A Survey of Machine Unlearning
by: Nguyen, Thanh Tam, et al.
Published: (2022)

Machine Unlearning of Traffic State Estimation and Prediction
by: Wang, Xin, et al.
Published: (2025)

On the Limitations and Prospects of Machine Unlearning for Generative AI
by: Zhou, Shiji, et al.
Published: (2024)

Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
by: Huang, Zhehao, et al.
Published: (2024)

KL-regularization Itself is Differentially Private in Bandits and RLHF
by: Zhang, Yizhou, et al.
Published: (2025)

When AI Eats Itself: On the Caveats of AI Autophagy
by: Xing, Xiaodan, et al.
Published: (2024)

SIMU: Selective Influence Machine Unlearning
by: Agarwal, Anu, et al.
Published: (2025)

An Information Theoretic Approach to Machine Unlearning
by: Foster, Jack, et al.
Published: (2024)

Verifying Machine Unlearning with Explainable AI
by: Vidal, Àlex Pujol, et al.
Published: (2024)

Towards Reliable Testing of Machine Unlearning
by: Mazhar, Anna, et al.
Published: (2026)

Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
by: Shaik, Thanveer, et al.
Published: (2023)

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
by: Zhang, Huishuai, et al.
Published: (2025)

DavIR: Data Selection via Implicit Reward for Large Language Models
by: Zhou, Haotian, et al.
Published: (2023)

Module-Aware Parameter-Efficient Machine Unlearning on Transformers
by: Bao, Wenjie, et al.
Published: (2025)

LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
by: Spartalis, Christoforos N., et al.
Published: (2025)