Joong-Ho Won

Joong-Ho Won

proudly crunching numbers.

Recent Publications

  • J. Won, K. Lange, J. Xu. A Unified Analysis of Convex and Non-convex $\ell_p$-ball Projection Problems. Optim. Lett. Accepted for publication. [preprint] [code]

  • J. Won, T. Zhang, H. Zhou. Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions. SIAM J. Optim. Accepted for publication. [preprint] [code]

  • Y. Lee, S. Lee, J. Won. Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. Int. Conf. Mach. Learn. (ICML 2022), PMLR 162:12423-12454, 2022. [ArXiv] for typo-corrected version.

  • S. Ko, H. Zhou, J. Zhou, J. Won. High-Performance Statistical Computing in the Computing Environments of the 2020s. Statistical Science, 2022, to appear. [preprint] [code]

  • S. Ko, G. X. Li, H. Choi, J. Won. Computationally Scalable Regression Modeling for Ultrahigh-dimensional Omics Data with ParProx. Brief. Bioinform. 22(6), bbab256, 2021. [code] [manual]

  • Y. Choi, S. Lee, J. Won. Learning from Nested Data with Ornstein Auto-Encoders. Int. Conf. Mach. Learn. (ICML 2021), PMLR 139:1943-1952, 2021.

  • J. Won, H. Zhou, K. Lange. Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality. SIAM J. Matrix Anal. Appl. 42(2), 859–882, 2021. [preprint] [code]

  • K. Lange, J. Won, A. Landeros, H. Zhou. Nonconvex Optimization via MM Algorithms: Convergence Theory. Wiley StatsRef. 2021.

  • J. Won. Proximity Operator of the Matrix Perspective Function and its Applications. Adv. Neural Inform. Process. Syst. 33 (NeurIPS 2020), 2020. [video] [code]

  • J. Won, S. Kim. Robust Trade-off Portfolio Selection. Optim. Eng. 21:867–904, 2020.

  • E. K. Ryu, S. Ko, J. Won. Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET. SIAM J. Sci. Comput. 42(1):B185-B206, 2020.

  • Y. Kwon, W. Kim, J. Won, M. C. Paik. Principled Learning Method for Wasserstein Distributionally Robust Optimization with Local Perturbations. Int. Conf. Mach. Learn. (ICML 2020), PMLR 119:5567-5576, 2020.

  • Y. Kwon, J. Won, B. J. Kim, M. C. Paik. Uncertainty Quantification using Bayesian Neural Networks in Classification: Application to Biomedical Image Segmentation. Comput. Stat. Data Anal. 142:106816, 2020.

  • J. Won, J. Xu, K. Lange. Projection onto Minkowski Sums with Application to Constrained Learning. Int. Conf. Mach. Learn. (ICML 2019), PMLR 97:3642-3651, 2019. [slides] [video] [code]

  • S. Ko, J. Won. Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. Int. Conf. Artif. Intell. Statist. (AISTATS 2019). PMLR 89:1185-1194, 2019. [code]

  • Y. Choi, J. Won. Ornstein Auto-Encoders. Int. Joint Conf. Artif. Intell. (IJCAI 2019), pp.2172-2178, 2019.

  • S. Ko, D. Yu, J. Won. Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, With Optimal Acceleration. J. Comput. Graph. Stat. 28(4):821-833, 2019. [code]

  • S. Kim, J. Lim, J. Won. Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. Int. Conf. Artif. Intell. Statist. (AISTATS 2018), PMLR 84:1495-1504, 2018. [code]

  • S. Winzeck, …, J. Won, et al. ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI. Front. Neurol. 9:679, 2018. First place in the ISLES Competition two years in a row: 2016 and 2017.

  • T. Lee, J. Won, J. Lim, S. Yoon. Large-Scale Structured Sparsity via Parallel Fused Lasso on Multiple GPUs. J. Comput. Graph. Stat. 26(4):851-864, 2017.

  • J. Won, X. Wu, S. H. Lee, Y. Lu. Cross-sectional Design with a Short-term Follow-up for Prognostic Imaging Biomarkers. Comput. Stat. Data Anal. 113:154-176, 2017.