Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearnin…
Graph EmbeddingKnowledge Graph EmbeddingLink PredictionMachine Unlearning+2Learning to Forget using Hypernetworks
Machine unlearning is gaining increasing attention as a way to remove adversarial data poisoning attacks from already trained models and to comply with privacy and AI regulations. The objective is to unlearn the effect o…
Data PoisoningMachine UnlearningMachine Unlearning reveals that the Gender-based Violence Victim Condition can be detected from Speech in a Speaker-Agnostic Setting
This study addresses the critical issue of gender-based violence's (GBV) impact on women's mental health. GBV, encompassing physical and sexual aggression, often results in long-lasting adverse effects for the victims, i…
Machine UnlearningSpeaker IdentificationFrom Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in c…
Ensemble LearningMachine UnlearningTowards Robust Evaluation of Unlearning in LLMs via Data Transformations
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; …
Machine UnlearningMUNBa: Machine Unlearning via Nash Bargaining
Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves balancing objectives related to forgetting s…
image-classificationImage ClassificationImage GenerationMachine Unlearning+1Verifying Machine Unlearning with Explainable AI
We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory compliance. With the increasing need to…
Feature ImportanceMachine UnlearningPromoting User Data Autonomy During the Dissolution of a Monopolistic Firm
The deployment of AI in consumer products is currently focused on the use of so-called foundation models, large neural networks pre-trained on massive corpora of digital records. This emphasis on scaling up datasets and …
Machine UnlearningProvable unlearning in topic modeling and downstream tasks
Machine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult. Provable guarantees for unlearning are …
Machine UnlearningTopic ModelsA Review on Machine Unlearning
Recently, an increasing number of laws have governed the useability of users' privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applic…
Machine UnlearningPrivacy PreservingLoRA Unlearns More and Retains More (Student Abstract)
Due to increasing privacy regulations and regulatory compliance, Machine Unlearning (MU) has become essential. The goal of unlearning is to remove information related to a specific class from a model. Traditional approac…
Computational EfficiencyMachine UnlearningNeural Corrective Machine Unranking
Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods to IR may compromise retrieval effectiven…
Information RetrievalMachine UnlearningRetrievalMachine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
Machine unlearning is new emerged technology that removes a subset of the training data from a trained model without affecting the model performance on the remaining data. This topic is becoming increasingly important in…
Machine UnlearningUnlearning in- vs. out-of-distribution data in LLMs under gradient-based method
Machine unlearning aims to solve the problem of removing the influence of selected training examples from a learned model. Despite the increasing attention to this problem, it remains an open research question how to eva…
Machine UnlearningMemorizationGame-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage
With the extensive use of machine learning technologies, data providers encounter increasing privacy risks. Recent legislation, such as GDPR, obligates organizations to remove requested data and its influence from a trai…
Machine UnlearningBenchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs)…
BenchmarkingLanguage ModelingLanguage ModellingMachine Unlearning+1Model Integrity when Unlearning with T2I Diffusion Models
The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes generate undesirable outputs. To mitigate …
Machine UnlearningmodelPseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning
Machine unlearning--enabling a trained model to forget specific data--is crucial for addressing biased data and adhering to privacy regulations like the General Data Protection Regulation (GDPR)'s "right to be forgotten"…
Machine UnlearningPrivacy PreservingIdentify Backdoored Model in Federated Learning via Individual Unlearning
Backdoor attacks present a significant threat to the robustness of Federated Learning (FL) due to their stealth and effectiveness. They maintain both the main task of the FL system and the backdoor task simultaneously, c…
Anomaly DetectionFederated LearningMachine UnlearningZero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specific data influences from trained models w…
Machine UnlearningPrivacy Preserving