Cover Image

Certified Machine Unlearning via Noisy Stochastic Gradient Descent

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chien, Eli, Wang, Haoyu, Chen, Ziang, Li, Pan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
by: Chien, Eli, et al.
Published: (2024)

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)

Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025)

A Robust Certified Machine Unlearning Method Under Distribution Shift
by: Guo, Jinduo, et al.
Published: (2026)

Federated Unlearning with Gradient Descent and Conflict Mitigation
by: Pan, Zibin, et al.
Published: (2024)

Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)

Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
by: Dufraiche, Jean, et al.
Published: (2026)

Verification of Machine Unlearning is Fragile
by: Zhang, Binchi, et al.
Published: (2024)

A Certified Unlearning Approach without Access to Source Data
by: Basaran, Umit Yigit, et al.
Published: (2025)

Machine Unlearning of Pre-trained Large Language Models
by: Yao, Jin, et al.
Published: (2024)

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

Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
by: Birrell, Jeremiah, et al.
Published: (2024)

Differentially Private Graph Diffusion with Applications in Personalized PageRanks
by: Wei, Rongzhe, et al.
Published: (2024)

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

Differentially Private Relational Learning with Entity-level Privacy Guarantees
by: Huang, Yinan, et al.
Published: (2025)

Distributional Machine Unlearning via Selective Data Removal
by: Allouah, Youssef, et al.
Published: (2025)

PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
by: Li, Xuran, et al.
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)

Towards Irreversible Machine Unlearning for Diffusion Models
by: Yuan, Xun, et al.
Published: (2025)

TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
by: Wang, Weiqi, et al.
Published: (2025)

Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
by: Greenewald, Kristjan, et al.
Published: (2024)

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
by: Lu, Bingguang, et al.
Published: (2025)

Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
by: Chen, Aobo, et al.
Published: (2026)

MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
by: Li, Xiang, et al.
Published: (2025)

Collective Certified Robustness against Graph Injection Attacks
by: Lai, Yuni, et al.
Published: (2024)

Unlearning Backdoor Attacks through Gradient-Based Model Pruning
by: Dunnett, Kealan, et al.
Published: (2024)

Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
by: Yin, Haoteng, et al.
Published: (2024)

Privacy Preservation through Practical Machine Unlearning
by: Dilworth, Robert
Published: (2025)

Forgetting Similar Samples: Can Machine Unlearning Do it Better?
by: Xu, Heng, et al.
Published: (2026)

Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
by: Zheng, Weidong, et al.
Published: (2026)

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
by: Bertran, Martin, et al.
Published: (2024)

SoK: Machine Unlearning for Large Language Models
by: Ren, Jie, et al.
Published: (2025)

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2024)

SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
by: Wang, Weiqi, et al.
Published: (2025)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

TruVRF: Towards Triple-Granularity Verification on Machine Unlearning
by: Zhou, Chunyi, et al.
Published: (2024)

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
by: Naderloui, Nima, et al.
Published: (2025)

Goldfish: An Efficient Federated Unlearning Framework
by: Wang, Houzhe, et al.
Published: (2024)