# Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models Source: [Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:10:33.419Z Authors: Guihong Li, Hsiang Hsu, Chun-Fu Chen, Radu Marculescu Published: 2023-12-22T18:55:45.000Z ## Abstract The rapid growth of machine learning has spurred legislative initiatives such as \`\`the Right to be Forgotten,'' allowing users to request data removal. In response, \`\`machine unlearning'' proposes the selective removal of unwanted data without the need for retraining from scratch. While the Neural-Tangent-Kernel-based (NTK-based) unlearning method excels in performance, it suffers from significant computational complexity, especially for large-scale models and datasets. Our work introduces \`\`Fast-NTK,'' a novel NTK-based unlearning algorithm that significantly reduces the computational complexity by incorporating parameter-efficient fine-tuning methods, such as fine-tuning batch normalization layers in a CNN or visual prompts in a vision transformer. Our experimental results demonstrate scalability to much larger neural networks and datasets (e.g., 88M parameters; 5k images), surpassing the limitations of previous full-model NTK-based approaches designed for smaller cases (e.g., 8M parameters; 500 images). Notably, our approach maintains a performance comparable to the traditional method of retraining on the retain set alone. Fast-NTK can thus enable for practical and scalable NTK-based unlearning in deep neural networks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Machine Unlearning on CIFAR-10 Random Forget 10% (train)](): Machine Unlearning; CIFAR-10 Random Forget 10% (train); Retain Accuracy: 99.45. ## Original sources and implementation [Original paper]() [PDF]()