:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Grimes, Keltin, Abidi, Collin, Frank, Cole, Gallagher, Shannon
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.19211
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing
by: Grimes, Keltin, et al.
Published: (2024)

Back to Blackwell: Closing the Loop on Intransitivity in Multi-Objective Preference Fine-Tuning
by: Zhang, Jiahao, et al.
Published: (2026)

The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
by: Xiao, Yang, et al.
Published: (2025)

Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM
by: Wu, Xiaoyu, et al.
Published: (2025)

From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
by: Yang, Yuncong, et al.
Published: (2024)

From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming
by: Sinha, Anusha, et al.
Published: (2025)

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

Label Smoothing Improves Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)

Mirror Mirror on the Wall, Have I Forgotten it All? A New Framework for Evaluating Machine Unlearning
by: Brimhall, Brennon, et al.
Published: (2025)

Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
by: Deng, Zhipeng, et al.
Published: (2024)

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

Benchmarking Federated Machine Unlearning methods for Tabular Data
by: Xiao, Chenguang, et al.
Published: (2025)

Improved Localized Machine Unlearning Through the Lens of Memorization
by: Torkzadehmahani, Reihaneh, et al.
Published: (2024)

Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models
by: X, Abdullah
Published: (2025)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
by: Jiang, Yu, et al.
Published: (2025)

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy
by: Sui, Zhihao, et al.
Published: (2025)

MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
by: Cheng, Jiali, et al.
Published: (2024)

Graph Neural Networks Gone Hogwild
by: Solodova, Olga, et al.
Published: (2024)

An Illusion of Unlearning? Assessing Machine Unlearning Through Internal Representations
by: Gao, Yichen, et al.
Published: (2026)

FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
by: Li, Zitong, et al.
Published: (2025)

Not All Tokens Are Meant to Be Forgotten
by: Zhou, Xiangyu, et al.
Published: (2025)

A Review on Machine Unlearning
by: Zhang, Haibo, et al.
Published: (2024)

Towards Natural Machine Unlearning
by: He, Zhengbao, et al.
Published: (2024)

Reference-Guided Machine Unlearning
by: Mirlach, Jonas, et al.
Published: (2026)

Machine Unlearning for Streaming Forgetting
by: Shen, Shaofei, et al.
Published: (2025)

Sharpness-Aware Machine Unlearning
by: Tang, Haoran, et al.
Published: (2025)

Fairness and Robustness in Machine Unlearning
by: Tran, Khoa, et al.
Published: (2025)

Adversarial Machine Unlearning
by: Di, Zonglin, et al.
Published: (2024)

Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
by: Lee, Hong kyu, et al.
Published: (2024)

Erase at the Core: Representation Unlearning for Machine Unlearning
by: Lee, Jaewon, et al.
Published: (2026)

Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
by: Chien, Eli, et al.
Published: (2024)

Revisiting Machine Unlearning with Dimensional Alignment
by: Seo, Seonguk, et al.
Published: (2024)

Dataset Condensation Driven Machine Unlearning
by: Khan, Junaid Iqbal
Published: (2024)

On the Impossibility of Retrain Equivalence in Machine Unlearning
by: Yu, Jiatong, et al.
Published: (2025)

Verifiable and Provably Secure Machine Unlearning
by: Eisenhofer, Thorsten, et al.
Published: (2022)

Efficient Verified Machine Unlearning For Distillation
by: Quan, Yijun, et al.
Published: (2025)

Statistical Roughness-Informed Machine Unlearning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Towards Source-Free Machine Unlearning
by: Ahmed, Sk Miraj, et al.
Published: (2025)

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

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