1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 1 cited
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model
Seok-Ju Hahn, Junghye Lee
Federated learning platforms are gaining popularity. One of the major benefits is to mitigate the privacy risks as the learning of algorithms can be achieved without collecting or…
cs.CR2020★ 1 cited
Secure and Differentially Private Bayesian Learning on Distributed Data
Yeongjae Gil, Xiaoqian Jiang, Miran Kim +1
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries. However, it is a non-trivial task as integrating data from multiple sources can p…
cs.LG2019
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data
Seok-Ju Hahn, Junghye Lee
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privac…