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2020 – today
- 2024
[c12]Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, Joong-Ho Won:
$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. ICLR 2024- [c11]Hyunjong Lee, Yedarm Seong, Sungdong Lee, Joong-Ho Won:
StrWAEs to Invariant Representations. ICML 2024 - [i6]Joong-Ho Won, Jihan Jung:
On the Correctness of the Generalized Isotonic Recursive Partitioning Algorithm. CoRR abs/2401.04847 (2024) - 2023
- [j17]Joong-Ho Won
, Kenneth Lange, Jason Xu:
A unified analysis of convex and non-convex ℓ p-ball projection problems. Optim. Lett. 17(5): 1133-1159 (2023) - [i5]Young-geun Kim, Kyungbok Lee, Youngwon Choi, Joong-Ho Won, Myunghee Cho Paik:
Wasserstein Geodesic Generator for Conditional Distributions. CoRR abs/2308.10145 (2023) - [i4]Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, Joong-Ho Won:
t3-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. CoRR abs/2312.01133 (2023) - 2022
- [j16]Sungdong Lee, Leonard Sunwoo, Youngwon Choi, Jae Hyup Jung, Seung Chai Jung, Joong-Ho Won:
Impact of Diffusion-Perfusion Mismatch on Predicting Final Infarction Lesion Using Deep Learning. IEEE Access 10: 97879-97887 (2022) - [j15]Joong-Ho Won, Teng Zhang, Hua Zhou:
Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions. SIAM J. Optim. 32(3): 2180-2207 (2022) - [c10]Yoonhyung Lee, Sungdong Lee, Joong-Ho Won:
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. ICML 2022: 12423-12454 - [i3]Yoonhyung Lee, Sungdong Lee, Joong-Ho Won:
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. CoRR abs/2206.12663 (2022) - 2021
- [j14]Seyoon Ko, Ginny X. Li, Hyungwon Choi, Joong-Ho Won:
Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx. Briefings Bioinform. 22(6) (2021) - [j13]Joong-Ho Won, Hua Zhou, Kenneth Lange:
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality. SIAM J. Matrix Anal. Appl. 42(2): 859-882 (2021) - [c9]Youngwon Choi, Sungdong Lee, Joong-Ho Won:
Learning from Nested Data with Ornstein Auto-Encoders. ICML 2021: 1943-1952 - 2020
- [j12]Yongchan Kwon, Joong-Ho Won, Beomjoon Kim, Myunghee Cho Paik:
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Comput. Stat. Data Anal. 142 (2020) - [j11]Sangoh Jeong, Hyun-Soo Kim, KyuWoon Kim, Byeong-Moon Jeon, Joong-Ho Won:
A real-time 3D video analyzer for enhanced 3D audio-visual systems. Multim. Syst. 26(2): 125-137 (2020) - [j10]Ernest K. Ryu, Seyoon Ko, Joong-Ho Won:
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET. SIAM J. Sci. Comput. 42(1): B185-B206 (2020) - [c8]Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik:
Principled learning method for Wasserstein distributionally robust optimization with local perturbations. ICML 2020: 5567-5576 - [c7]Joong-Ho Won:
Proximity Operator of the Matrix Perspective Function and its Applications. NeurIPS 2020 - [i2]Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik:
Principled Learning Method for Wasserstein distributionally robust optimization with local perturbations. CoRR abs/2006.03333 (2020) - [i1]Seyoon Ko, Hua Zhou, Jin Zhou, Joong-Ho Won:
DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia. CoRR abs/2010.16114 (2020)
2010 – 2019
- 2019
- [c6]Seyoon Ko, Joong-Ho Won:
Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. AISTATS 2019: 1185-1194 - [c5]Joong-Ho Won, Jason Xu, Kenneth Lange:
Projection onto Minkowski Sums with Application to Constrained Learning. ICML 2019: 3642-3651 - [c4]Youngwon Choi, Joong-Ho Won:
Ornstein Auto-Encoders. IJCAI 2019: 2172-2178 - 2018
- [j9]Baekjin Kim, Donghyeon Yu, Joong-Ho Won:
Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. Appl. Intell. 48(8): 1933-1952 (2018) - [j8]Taehoon Lee, Sungmin Lee, Woo Young Sim, Yu Mi Jung, Sunmi Han, Joong-Ho Won, Hyeyoung Min, Sungroh Yoon:
HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment. BMC Bioinform. 19-S(1): 49-61 (2018) - [j7]Taehoon Lee, Sungmin Lee, Woo Young Sim, Yu Mi Jung, Sunmi Han, Joong-Ho Won, Hyeyoung Min, Sungroh Yoon:
Correction to: HiComet: a high-throughput comet analysis tool for large-scale DNA damage assessment. BMC Bioinform. 19(1): 170:1 (2018) - [j6]Joungyoun Kim, Donghyeon Yu, Johan Lim, Joong-Ho Won:
A peeling algorithm for multiple testing on a random field. Comput. Stat. 33(1): 503-525 (2018) - [j5]Seunghyun Park, Hyun-Soo Choi, Byunghan Lee, Jongsik Chun, Joong-Ho Won, Sungroh Yoon:
hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction. IEEE ACM Trans. Comput. Biol. Bioinform. 15(2): 441-451 (2018) - [c3]Seung-Jean Kim, Johan Lim, Joong-Ho Won:
Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. AISTATS 2018: 1495-1504 - 2017
- [j4]Joong-Ho Won, Xiao Wu, Sang Han Lee, Ying Lu:
Cross-sectional design with a short-term follow-up for prognostic imaging biomarkers. Comput. Stat. Data Anal. 113: 154-176 (2017) - 2016
- [c2]Youngwon Choi, Yongchan Kwon, Han-Byul Lee, Beomjoon Kim, Myunghee Cho Paik, Joong-Ho Won:
Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. BrainLes@MICCAI 2016: 231-243 - 2013
- [j3]Yongkweon Jeon, Joong-Ho Won, Sungroh Yoon:
Massively Parallel Energy Space Exploration for Uncluttered Visualization of Vascular Structures. IEEE Trans. Biomed. Eng. 60(1): 240-244 (2013) - [j2]Joong-Ho Won, Yongkweon Jeon, Jarrett Rosenberg, Sungroh Yoon, Geoffrey D. Rubin, Sandy Napel:
Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming. IEEE Trans. Vis. Comput. Graph. 19(1): 81-93 (2013) - 2012
- [j1]Johan Lim, Joong-Ho Won:
ROC convex hull and nonparametric maximum likelihood estimation. Mach. Learn. 88(3): 433-444 (2012)
2000 – 2009
- 2006
- [c1]Joong-Ho Won, Geoffrey D. Rubin, Sandy Napel:
Flattening the Abdominal Aortic Tree for Effective Visualization. EMBC 2006: 3345-3348
Coauthor Index
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