2 citations · 3 across the 7 of their papers we have counts for
8 papers
A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Chenhao Zhang, Chris Lin, Su-In Lee
We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs). While SAEs improve interpreta…
Unlearning Evaluation through Subset Statistical Independence
Chenhao Zhang, Muxing Li, Feng Liu +2
Evaluating machine unlearning remains challenging, as existing methods typically require retraining reference models or performing membership inference attacks, both of which rely…
Machine Unlearning for Streaming Forgetting
Shaofei Shen, Chenhao Zhang, Yawen Zhao +3
Machine unlearning aims to remove knowledge of the specific training data in a well-trained model. Currently, machine unlearning methods typically handle all forgetting data in a s…
Toward Efficient Data-Free Unlearning
Chenhao Zhang, Shaofei Shen, Weitong Chen +1
Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic sampl…
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
Chenhao Zhang, Shaofei Shen, Yawen Zhao +2
With the increasing emphasis on data privacy, the significance of machine unlearning has grown substantially. Class unlearning, which involves enabling a trained model to forget da…
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
Shaofei Shen, Chenhao Zhang, Yawen Zhao +3
Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model. With the increasing emphasis on da…
CaMU: Disentangling Causal Effects in Deep Model Unlearning
Shaofei Shen, Chenhao Zhang, Alina Bialkowski +2
Machine unlearning requires removing the information of forgetting data while keeping the necessary information of remaining data. Despite recent advancements in this area, existin…
A Boosting Algorithm for Positive-Unlabeled Learning
Yawen Zhao, Mingzhe Zhang, Chenhao Zhang +3
Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural n…