Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
Toward Scalable Graph Unlearning: A Node Influence Maximization based Approach
Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in thriving graph-based scenarios. However,…
Machine UnlearningTechnical Report for the Forgotten-by-Design Project: Targeted Obfuscation for Machine Learning
The right to privacy, enshrined in various human rights declarations, faces new challenges in the age of artificial intelligence (AI). This paper explores the concept of the Right to be Forgotten (RTBF) within AI systems…
Inference AttackMachine UnlearningMembership Inference AttackPrivacy PreservingTowards Aligned Data Forgetting via Twin Machine Unlearning
Modern privacy regulations have spurred the evolution of machine unlearning, a technique enabling a trained model to efficiently forget specific training data. In prior unlearning methods, the concept of "data forgetting…
Machine UnlearningOpen Problems in Machine Unlearning for AI Safety
As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and healthcare, ensuring their safety and alignment with human values is param…
Machine UnlearningOpenGU: A Comprehensive Benchmark for Graph Unlearning
Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive information from trained graph neural networ…
Machine UnlearningThe Illusion of Unlearning: The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models
Text-to-image models such as Stable Diffusion, DALL*E, and Midjourney have gained immense popularity lately. However, they are trained on vast amounts of data that may include private, explicit, or copyrighted materi…
Machine UnlearningPerson De-reidentification: A Variation-guided Identity Shift Modeling
Person re-identification (ReID) is to associate images of individuals from different camera views against cross-view variations. Like other surveillance technologies, Re-ID faces serious privacy challenges, particula…
Face RecognitionMachine UnlearningPerson Re-IdentificationTowards Source-Free Machine Unlearning
As machine learning become more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasingly critical requirement. Existing u…
Machine UnlearningMulti-Objective Large Language Model Unlearning
Machine unlearning in the domain of large language models (LLMs) has attracted great attention recently, which aims to effectively eliminate undesirable behaviors from LLMs without full retraining from scratch. In this p…
Language ModelingLanguage ModellingLarge Language ModelMachine Unlearning+1A Comparative Study of Machine Unlearning Techniques for Image and Text Classification Models
Machine Unlearning has emerged as a critical area in artificial intelligence, addressing the need to selectively remove learned data from machine learning models in response to data privacy regulations. This paper provid…
Machine Unlearningtext-classificationText ClassificationForget Vectors at Play: Universal Input Perturbations Driving Machine Unlearning in Image Classification
Machine unlearning (MU), which seeks to erase the influence of specific unwanted data from already-trained models, is becoming increasingly vital in model editing, particularly to comply with evolving data regulations li…
image-classificationImage ClassificationMachine UnlearningModel EditingA hybrid framework for effective and efficient machine unlearning
Recently machine unlearning (MU) is proposed to remove the imprints of revoked samples from the already trained model parameters, to solve users' privacy concern. Different from the runtime expensive retraining from scra…
Machine UnlearningToward Efficient Data-Free Unlearning
Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic samples containing forgetting information but…
Machine UnlearningA Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
Recommender systems have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. Meanwhile, the widespread adoption of machine learning models i…
Machine UnlearningRecommendation SystemsSurveyThe Utility and Complexity of in- and out-of-Distribution Machine Unlearning
Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches a…
Machine UnlearningModerating the Generalization of Score-based Generative Model
Score-based Generative Models (SGMs) have demonstrated remarkable generalization abilities, e.g. generating unseen, but natural data. However, the greater the generalization power, the more likely the unintended generali…
Image GenerationImage InpaintingMachine UnlearningmodelMachine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice
We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy. These aspirations ar…
Machine UnlearningBoosting Alignment for Post-Unlearning Text-to-Image Generative Models
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful or inappropriate content and raises copyr…
Image GenerationMachine UnlearningImproved Localized Machine Unlearning Through the Lens of Memorization
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is important for key applications, including …
Machine UnlearningMemorizationSiamese Machine Unlearning with Knowledge Vaporization and Concentration
In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points fr…
Machine Unlearning