Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
Mirror Mirror on the Wall, Have I Forgotten it All? A New Framework for Evaluating Machine Unlearning
Machine unlearning methods take a model trained on a dataset and a forget set, then attempt to produce a model as if it had only been trained on the examples not in the forget set. We empirically show that an adversary i…
AllMachine UnlearningMUBox: A Critical Evaluation Framework of Deep Machine Unlearning
Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned informat…
Machine UnlearningFederated Large Language Models: Feasibility, Robustness, Security and Future Directions
The integration of Large Language Models (LLMs) and Federated Learning (FL) presents a promising solution for joint training on distributed data while preserving privacy and addressing data silo issues. However, this eme…
Federated LearningFew-Shot LearningMachine UnlearningSurveyCertified Data Removal Under High-dimensional Settings
Machine unlearning focuses on the computationally efficient removal of specific training data from trained models, ensuring that the influence of forgotten data is effectively eliminated without the need for full retrain…
Machine UnlearningEfficient Machine Unlearning by Model Splitting and Core Sample Selection
Machine unlearning is essential for meeting legal obligations such as the right to be forgotten, which requires the removal of specific data from machine learning models upon request. While several approaches to unlearni…
Machine UnlearningUnilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation
This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall …
Machine UnlearningOBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
Large language models (LLMs) trained over extensive corpora risk memorizing sensitive, copyrighted, or toxic content. To address this, we propose OBLIVIATE, a robust unlearning framework that removes targeted data while …
Machine UnlearningMemorizationMemory-Efficient Distributed Unlearning
Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data available at a set of remote users, unl…
Machine UnlearningErased but Not Forgotten: How Backdoors Compromise Concept Erasure
The expansion of large-scale text-to-image diffusion models has raised growing concerns about their potential to generate undesirable or harmful content, ranging from fabricated depictions of public figures to sexually e…
Backdoor AttackMachine UnlearningVerifying Robust Unlearning: Probing Residual Knowledge in Unlearned Models
Machine Unlearning (MUL) is crucial for privacy protection and content regulation, yet recent studies reveal that traces of forgotten information persist in unlearned models, enabling adversaries to resurface removed kno…
Machine UnlearningFairness and Robustness in Machine Unlearning
Machine unlearning poses the challenge of ``how to eliminate the influence of specific data from a pretrained model'' in regard to privacy concerns. While prior research on approximated unlearning has demonstrated accura…
FairnessMachine UnlearningSHA256 at SemEval-2025 Task 4: Selective Amnesia -- Constrained Unlearning for Large Language Models via Knowledge Isolation
Large language models (LLMs) frequently memorize sensitive information during training, posing risks when deploying publicly accessible models. Current machine unlearning methods struggle to selectively remove specific d…
AttributeMachine UnlearningMMLUWhen is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers
Task arithmetic refers to editing the pre-trained model by adding a weighted sum of task vectors, each of which is the weight update from the pre-trained model to fine-tuned models for certain tasks. This approach recent…
Binary ClassificationDomain GeneralizationLarge Language ModelMachine Unlearning+4LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks
Large language model unlearning has become a critical challenge in ensuring safety and controlled model behavior by removing undesired data-model influences from the pretrained model while preserving general utility. Sig…
Large Language ModelMachine UnlearningSAEs $\textit{Can}$ Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs
Machine unlearning is a promising approach to improve LLM safety by removing unwanted knowledge from the model. However, prevailing gradient-based unlearning methods suffer from issues such as high computational costs, h…
Computational Efficiencyfeature selectionMachine UnlearningPreserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition
Handwritten Text Recognition (HTR) is essential for document analysis and digitization. However, handwritten data often contains user-identifiable information, such as unique handwriting styles and personal lexicon choic…
Handwritten Text RecognitionHTRMachine UnlearningNot All Data Are Unlearned Equally
Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context of large language models (LLMs), unlearning has recently received increased att…
AllMachine UnlearningSky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning
The Internet of Drones (IoD), where drones collaborate in data collection and analysis, has become essential for applications such as surveillance and environmental monitoring. Federated learning (FL) enables drones to t…
Data PoisoningFederated LearningMachine UnlearningPrompting Forgetting: Unlearning in GANs via Textual Guidance
State-of-the-art generative models exhibit powerful image-generation capabilities, introducing various ethical and legal challenges to service providers hosting these models. Consequently, Content Removal Techniques (CRT…
AttributeImage GenerationMachine UnlearningBenchmarking Federated Machine Unlearning methods for Tabular Data
Machine unlearning, which enables a model to forget specific data upon request, is increasingly relevant in the era of privacy-centric machine learning, particularly within federated learning (FL) environments. This pape…
BenchmarkingComputational EfficiencyFederated LearningMachine Unlearning+1