3 citations · 4 across the 4 of their papers we have counts for
4 papers
Erased, but Not Gone: Output Forgetting Is Not True Forgetting
Teresa Pui Yee Yong, Win Kent Ong, Chee Seng Chan
Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced membership inference. But if output-level success can coexist with retra…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…
Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity
Hanlin Gu, Win Kent Ong, Chee Seng Chan +1
The advent of Federated Learning (FL) highlights the practical necessity for the right to be forgotten for all clients, allowing them to request data deletion from the machine lear…
IPR-NeRF: Ownership Verification meets Neural Radiance Field
Win Kent Ong, Kam Woh Ng, Chee Seng Chan +2
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressi…