Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
Mitigating Memorization In Language Models
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training da…
Machine UnlearningMemorizationTowards Understanding the Feasibility of Machine Unlearning
In light of recent privacy regulations, machine unlearning has attracted significant attention in the research community. However, current studies predominantly assess the overall success of unlearning approaches, overlo…
Machine UnlearningDeep Unlearn: Benchmarking Machine Unlearning
Machine unlearning (MU) aims to remove the influence of particular data points from the learnable parameters of a trained machine learning model. This is a crucial capability in light of data privacy requirements, trustw…
BenchmarkingMachine UnlearningDynFrs: An Efficient Framework for Machine Unlearning in Random Forest
Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, …
ClassificationMachine UnlearningUnified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
Machine unlearning (MU) has emerged to enhance the privacy and trustworthiness of deep neural networks. Approximate MU is a practical method for large-scale models. Our investigation into approximate MU starts with ident…
Machine UnlearningEfficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats
Multimodal contrastive learning uses various data modalities to create high-quality features, but its reliance on extensive data sources on the Internet makes it vulnerable to backdoor attacks. These attacks insert malic…
Backdoor Attackbackdoor defenseContrastive LearningMachine UnlearningSurvey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce
This paper surveys the landscape of security and data attacks on machine unlearning, with a focus on financial and e-commerce applications. We discuss key privacy threats such as Membership Inference Attacks and Data Rec…
Data PoisoningMachine UnlearningAn Adversarial Perspective on Machine Unlearning for AI Safety
Large language models are finetuned to refuse questions about hazardous knowledge, but these protections can often be bypassed. Unlearning methods aim at completely removing hazardous capabilities from models and make th…
Machine UnlearningDistribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting
With the explosive growth of deep learning applications and increasing privacy concerns, the right to be forgotten has become a critical requirement in various AI industries. For example, given a facial recognition syste…
Machine UnlearningAlternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large Language Models (LLMs) face a critical cha…
Machine UnlearningScore Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models
The machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) as a crucial foundation for developing safe…
Machine UnlearningRewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions
Machine unlearning algorithms aim to efficiently remove data from a model without retraining it from scratch, in order to remove corrupted or outdated data or respect a user's ``right to be forgotten." Certified machine …
Machine UnlearningEnhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning
In the realm of multimedia data analysis, the extensive use of image datasets has escalated concerns over privacy protection within such data. Current research predominantly focuses on privacy protection either in data s…
AttributeMachine UnlearningAccurate Forgetting for All-in-One Image Restoration Model
Privacy protection has always been an ongoing topic, especially for AI. Currently, a low-cost scheme called Machine Unlearning forgets the private data remembered in the model. Specifically, given a private dataset and a…
AllImage RestorationMachine UnlearningUnified Image RestorationForget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distri…
Language ModelingLanguage ModellingMachine UnlearningLLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet
Recent large language model (LLM) defenses have greatly improved models' ability to refuse harmful queries, even when adversarially attacked. However, LLM defenses are primarily evaluated against automated adversarial at…
Language ModelingLanguage ModellingLarge Language ModelMachine Unlearning+1CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a pot…
FairnessMachine UnlearningRecommendation SystemsUnlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code
This work investigates the application of Machine Unlearning (MU) for mitigating the impact of trojans embedded in conventional large language models of natural language (Text-LLMs) and large language models of code (Cod…
Defect DetectionMachine UnlearningSentiment AnalysisTowards Aligned Data Removal via Twin Machine Unlearning
Modern privacy regulations have spurred the evolution of machine unlearning, a technique that enables the removal of data from an already trained ML model without requiring retraining from scratch. Previous unlearning me…
Machine UnlearningA Unified Framework for Continual Learning and Unlearning
Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unl…
Continual LearningKnowledge DistillationMachine Unlearning