no code implementations • 31 May 2024 • SeungHwan An, Gyeongdong Woo, Jaesung Lim, Changhyun Kim, Sungchul Hong, Jong-June Jeon
In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu).
no code implementations • 30 May 2024 • Sungchul Hong, SeungHwan An, Jong-June Jeon
We investigate the problem of the generative model for imbalanced classification and introduce a framework to enhance the SMOTE algorithm using Variational Autoencoders (VAE).
1 code implementation • 7 May 2024 • JaeSung Park, Sungchul Hong, Yoonseo Cho, Jong-June Jeon
Sea ice at the North Pole is vital to global climate dynamics.
no code implementations • 6 Dec 2023 • SeungHwan An, Sungchul Hong, Jong-June Jeon
This measure enables us to capture both marginal and joint distributional information simultaneously, as it incorporates a mixture measure with point masses on standard basis vectors.
no code implementations • 2 Mar 2023 • Sungchul Hong, Jong-June Jeon
However, estimating an optimal portfolio assessed by a pessimistic risk is still challenging due to the absence of a computationally tractable model.