Seok-Ju Hahn

According to our database, Seok-Ju Hahn authored at least 7 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Algorithms for Collaborative Machine Learning under Statistical Heterogeneity.

Seok-Ju Hahn

CoRR, 2024

CAFO: Feature-Centric Explanation on Time Series Classification.

Jaeho Kim, Seok-Ju Hahn, Yoontae Hwang, Junghye Lee, Seulki Lee

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Pursuing Overall Welfare in Federated Learning through Sequential Decision Making.

Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

Connecting Low-Loss Subspace for Personalized Federated Learning.

Seok-Ju Hahn, Minwoo Jeong, Junghye Lee

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Subspace Learning for Personalized Federated Optimization.

Seok-Ju Hahn, Minwoo Jeong, Junghye Lee

CoRR, 2021

2020

GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model.

Seok-Ju Hahn, Junghye Lee

CoRR, 2020

2019

Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data.

Seok-Ju Hahn, Junghye Lee

CoRR, 2019