Penalized principal logistic regression for sparse sufficient dimension reduction. - Seung Jun Shin; Andreas Artemiou (Comput. Stat. Data Anal., 2017)

Stability approach to selecting the number of principal components. - Jiyeon Song; Seung Jun Shin (Comput. Stat., 2018)

The evolutionary history of 2,658 cancers. - Moritz Gerstung; Clemency Jolly; Ignaty Leshchiner; Stefan C. Dentro; Santiago Gonzalez; Thomas J. Mitchell; Yulia Rubanova; Pavana Anur; Kaixian Yu; Maxime Tarabichi; Amit G. Deshwar; Jeff Wintersinger; et al. (Nat., 2020)

A gradient-based variable selection for binary classification in reproducing kernel Hilbert space. - Jongkyeong Kang; Seung Jun Shin (CoRR, 2021)

Real-time sufficient dimension reduction through principal least squares support vector machines. - Andreas Artemiou; Yuexiao Dong; Seung Jun Shin (Pattern Recognit., 2021)

A Forward Approach for Sufficient Dimension Reduction in Binary Classification. - Jongkyeong Kang; Seung Jun Shin (J. Mach. Learn. Res., 2022)

Principal weighted least square support vector machine: An online dimension-reduction tool for binary classification. - Hyun Jung Jang; Seung Jun Shin; Andreas Artemiou (Comput. Stat. Data Anal., 2023)

A least distance estimator for a multivariate regression model using deep neural networks. - Jungmin Shin; Seung Jun Shin; Sungwan Bang (CoRR, 2024)

Simultaneous estimation and variable selection for a non-crossing multiple quantile regression using deep neural networks. - Jungmin Shin; Seunghyun Gwak; Seung Jun Shin; Sungwan Bang (Stat. Comput., 2024)