3 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
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…