21 citations · 32 across the 5 of their papers we have counts for
6 papers
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity
Seok-Ju Hahn
Learning from distributed data without accessing them is undoubtedly a challenging and non-trivial task. Nevertheless, the necessity for distributed training of a statistical model…
CAFO: Feature-Centric Explanation on Time Series Classification
Jaeho Kim, Seok-Ju Hahn, Yoontae Hwang +2
In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimension…
Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system h…
Connecting Low-Loss Subspace for Personalized Federated Learning
Seok-Ju Hahn, Minwoo Jeong, Junghye Lee
Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of fede…
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…
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…