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Machine Unlearning

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Showing 351–400 of 438 papers

TitleStatusHype
Machine Unlearning for Recommendation Systems: An Insight—0
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization—0
Federated Unlearning for Human Activity Recognition—0
Scissorhands: Scrub Data Influence via Connection Sensitivity in NetworksCode0
Machine unlearning through fine-grained model parameters perturbation—0
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning—0
Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation—0
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models—0
FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative ModelsCode0
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity—0
Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient ProjectionCode0
DUCK: Distance-based Unlearning via Centroid KinematicsCode0
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective—0
Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges—0
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning—0
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation—0
MultiDelete for Multimodal Machine UnlearningCode0
DeepClean: Machine Unlearning on the Cheap by Resetting Privacy Sensitive Weights using the Fisher Diagonal—0
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach—0
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine UnlearningCode0
Open Knowledge Base Canonicalization with Multi-task Unlearning—0
Fast Model Debias with Machine Unlearning—0
Unlearning with Fisher Masking—0
Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks—0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning—0
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System—0
Tight Bounds for Machine Unlearning via Differential Privacy—0
Machine Unlearning Methodology base on Stochastic Teacher Network—0
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation—0
Machine Unlearning for Causal Inference—0
Generative Adversarial Networks Unlearning—0
Fair Machine Unlearning: Data Removal while Mitigating DisparitiesCode0
From Adaptive Query Release to Machine Unlearning—0
Gradient Surgery for One-shot Unlearning on Generative Model—0
Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions—0
Federated Unlearning via Active Forgetting—0
One-Shot Machine Unlearning with Mnemonic CodeCode0
Machine Unlearning: A Survey—0
Machine Unlearning: its nature, scope, and importance for a "delete culture"—0
Random Relabeling for Efficient Machine Unlearning—0
Learn to Unlearn: A Survey on Machine Unlearning—0
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy—0
SAFE: Machine Unlearning With Shard Graphs—0
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning—0
Inductive Graph UnlearningCode0
Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search—0
Multi-Class Unlearning for Image Classification via Weight Filtering—0
Boundary Unlearning—0
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations—0
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods—0
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