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2020 – today
- 2024
[c4]Bonwoo Lee, Jeongyoun Ahn, Cheolwoo Park:
Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features. NeurIPS 2024- 2023
- [j13]Binglin Li, Changwon Yoon, Jeongyoun Ahn:
Reproducing Kernels and New Approaches in Compositional Data Analysis. J. Mach. Learn. Res. 24: 327:1-327:34 (2023) - [c3]Junyoung Park, Jeongyoun Ahn, Cheolwoo Park:
Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation. ICML 2023: 27034-27047 - [c2]Bonwoo Lee, Jeongyoun Ahn, Cheolwoo Park:
Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy. NeurIPS 2023 - 2022
- [j12]J. C. Poythress, Jeongyoun Ahn, Cheolwoo Park
:
Low-rank, Orthogonally Decomposable Tensor Regression With Application to Visual Stimulus Decoding of fMRI Data. J. Comput. Graph. Stat. 31(1): 190-203 (2022) - [c1]Junyoung Park, Changwon Yoon, Cheolwoo Park, Jeongyoun Ahn:
Kernel Methods for Radial Transformed Compositional Data with Many Zeros. ICML 2022: 17458-17472 - [i1]Binglin Li, Jeongyoun Ahn:
Reproducing Kernels and New Approaches in Compositional Data Analysis. CoRR abs/2205.01158 (2022) - 2021
- [j11]Ziyang Ma, Jeongyoun Ahn:
Feature-weighted ordinal classification for predicting drug response in multiple myeloma. Bioinform. 37(19): 3270-3276 (2021) - [j10]Jeongyoun Ahn, Hee Cheol Chung, Yongho Jeon:
Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination. J. Comput. Graph. Stat. 30(1): 192-203 (2021) - [j9]Hee Cheol Chung, Jeongyoun Ahn:
Subspace rotations for high-dimensional outlier detection. J. Multivar. Anal. 183: 104713 (2021) - 2020
- [j8]Debin Qiu, Jeongyoun Ahn:
Grouped variable screening for ultra-high dimensional data for linear model. Comput. Stat. Data Anal. 144: 106894 (2020)
2010 – 2019
- 2016
- [j7]Sandra E. Safo, Jeongyoun Ahn:
General sparse multi-class linear discriminant analysis. Comput. Stat. Data Anal. 99: 81-90 (2016) - 2015
- [j6]Jeongyoun Ahn, Yongho Jeon:
Sparse HDLSS discrimination with constrained data piling. Comput. Stat. Data Anal. 90: 74-83 (2015) - [j5]Yongho Jeon, Jeongyoun Ahn, Cheolwoo Park:
A Nonparametric Kernel Approach to Interval-Valued Data Analysis. Technometrics 57(4): 566-575 (2015) - 2012
- [j4]Jeongyoun Ahn, Muliang Peng, Cheolwoo Park, Yongho Jeon:
A resampling approach for interval-valued data regression. Stat. Anal. Data Min. 5(4): 336-348 (2012) - 2010
- [j3]Jeongyoun Ahn:
A stable hyperparameter selection for the Gaussian RBF kernel for discrimination. Stat. Anal. Data Min. 3(3): 142-148 (2010)
2000 – 2009
- 2007
- [j2]Yufeng Liu, Hao Helen Zhang, Cheolwoo Park, Jeongyoun Ahn:
Support vector machines with adaptive Lq penalty. Comput. Stat. Data Anal. 51(12): 6380-6394 (2007) - 2006
- [j1]Hao Helen Zhang, Jeongyoun Ahn, Xiaodong Lin, Cheolwoo Park:
Gene selection using support vector machines with non-convex penalty. Bioinform. 22(1): 88-95 (2006)
Coauthor Index
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