18 citations · 19 across the 4 of their papers we have counts for
4 papers
Module-Aware Parameter-Efficient Machine Unlearning on Transformers
Wenjie Bao, Jian Lou, Yuke Hu +5
Transformer has become fundamental to a vast series of pre-trained large models that have achieved remarkable success across diverse applications. Machine unlearning, which focuses…
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Bocheng Ju, Junchao Fan, Jiaqi Liu +1
Federated learning enables collaborative machine learning while preserving data privacy. However, the rise of federated unlearning, designed to allow clients to erase their data fr…
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
Jiaqi Liu, Jian Lou, Zhan Qin +1
We study the problem of -certified machine unlearning for minimax models. Most of the existing works focus on unlearning from standard statistical learning models that have…
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Yuke Hu, Jian Lou, Jiaqi Liu +4
Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging demand for supporting Machine Learning-driven services to offer revolutionized user experience acr…