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ORCIDYongdai Kim
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2020 – today
- 2024
[c13]Dongha Kim, Yongchan Choi, Kunwoong Kim, Ilsang Ohn, Yongdai Kim:
IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples. AAAI 2024: 13104-13112- [c12]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models. ICML 2024 - [i21]Insung Kong, Yongdai Kim:
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights. CoRR abs/2403.14225 (2024) - [i20]Yongchan Choi, Seokhun Park, Chanmoo Park, Dongha Kim, Yongdai Kim:
META-ANOVA: Screening interactions for interpretable machine learning. CoRR abs/2408.00973 (2024) - 2023
- [j29]Kuhwan Jeong, Minwoo Chae, Yongdai Kim:
Online learning for the Dirichlet process mixture model via weakly conjugate approximation. Comput. Stat. Data Anal. 179: 107626 (2023) - [j28]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models. J. Mach. Learn. Res. 24: 77:1-77:42 (2023) - [j27]Seonghyeon Kim, Sara Kim, Kunwoong Kim, Yongdai Kim
:
Lq regularization for fair artificial intelligence robust to covariate shift. Stat. Anal. Data Min. 16(3): 237-256 (2023) - [c11]Dongyoon Yang, Insung Kong, Yongdai Kim:
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation. ICCV 2023: 4529-4538 - [c10]Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim:
Covariate balancing using the integral probability metric for causal inference. ICML 2023: 17430-17461 - [c9]Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim:
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. ICML 2023: 17462-17491 - [c8]Dongyoon Yang, Insung Kong, Yongdai Kim:
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples. ICML 2023: 39331-39348 - [i19]Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim:
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. CoRR abs/2301.04257 (2023) - [i18]Sara Kim, Kyusang Yu, Yongdai Kim:
Within-group fairness: A guidance for more sound between-group fairness. CoRR abs/2301.08375 (2023) - [i17]Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim:
Covariate balancing using the integral probability metric for causal inference. CoRR abs/2305.13715 (2023) - [i16]Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim:
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. CoRR abs/2305.14765 (2023) - [i15]Ilsang Ohn, Lizhen Lin, Yongdai Kim:
A Bayesian sparse factor model with adaptive posterior concentration. CoRR abs/2305.18488 (2023) - [i14]Dongyoon Yang, Kunwoong Kim, Yongdai Kim:
Improving Performance of Semi-Supervised Learning by Adversarial Attacks. CoRR abs/2308.04018 (2023) - [i13]Dongyoon Yang, Insung Kong, Yongdai Kim:
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation. CoRR abs/2308.04061 (2023) - 2022
- [j26]Ilsang Ohn, Yongdai Kim:
Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality. Neural Comput. 34(2): 476-517 (2022) - [j25]Kunwoong Kim, Ilsang Ohn, Sara Kim, Yongdai Kim:
SLIDE: A surrogate fairness constraint to ensure fairness consistency. Neural Networks 154: 441-454 (2022) - [j24]Woosung Kim, Seonghyeon Kim, Myung Hwan Na, Yongdai Kim:
A modified least angle regression algorithm for interaction selection with heredity. Stat. Anal. Data Min. 15(5): 630-647 (2022) - [c7]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. ICML 2022: 11074-11101 - [i12]Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim:
Learning fair representation with a parametric integral probability metric. CoRR abs/2202.02943 (2022) - [i11]Kunwoong Kim, Ilsang Ohn, Sara Kim, Yongdai Kim:
SLIDE: a surrogate fairness constraint to ensure fairness consistency. CoRR abs/2202.03165 (2022) - [i10]Dongyoon Yang, Insung Kong, Yongdai Kim:
Adaptive Regularization for Adversarial Training. CoRR abs/2206.03353 (2022) - 2021
- [j23]Dongha Kim, Yongdai Kim:
Understanding Effects of Architecture Design to Invariance and Complexity in Deep Neural Networks. IEEE Access 9: 9670-9681 (2021) - [j22]Sang Jun Moon, Jong-June Jeon, Jason Sang Hun Lee, Yongdai Kim:
Learning Multiple Quantiles With Neural Networks. J. Comput. Graph. Stat. 30(4): 1238-1248 (2021) - [j21]Yongdai Kim, Ilsang Ohn, Dongha Kim:
Fast convergence rates of deep neural networks for classification. Neural Networks 138: 179-197 (2021) - [c6]Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik:
Kernel-convoluted Deep Neural Networks with Data Augmentation. AAAI 2021: 8155-8162 - [i9]Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin:
A likelihood approach to nonparametric estimation of a singular distribution using deep generative models. CoRR abs/2105.04046 (2021) - [i8]Dongha Kim, Yongchan Choi, Kunwoong Kim, Yongdai Kim:
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks. CoRR abs/2106.15185 (2021) - 2020
- [j20]Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi:
Primal path algorithm for compositional data analysis. Comput. Stat. Data Anal. 148: 106958 (2020) - [c5]Dongha Kim, Jaesung Hwang, Yongdai Kim:
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models. AISTATS 2020: 2153-2163 - [i7]Ilsang Ohn, Yongdai Kim:
Nonconvex sparse regularization for deep neural networks and its optimality. CoRR abs/2003.11769 (2020) - [i6]Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik:
Kernel-convoluted Deep Neural Networks with Data Augmentation. CoRR abs/2012.02521 (2020)
2010 – 2019
- 2019
- [j19]Ilsang Ohn, Yongdai Kim:
Smooth Function Approximation by Deep Neural Networks with General Activation Functions. Entropy 21(7): 627 (2019) - [j18]Dongha Kim, JongRoul Woo, Jungwoo Shin, Jongsu Lee, Yongdai Kim:
Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market. Ind. Manag. Data Syst. 119(5): 1089-1103 (2019) - [i5]Ilsang Ohn, Yongdai Kim:
Smooth function approximation by deep neural networks with general activation functions. CoRR abs/1906.06903 (2019) - [i4]Dongha Kim, Yongchan Choi, Yongdai Kim:
Understanding and Improving Virtual Adversarial Training. CoRR abs/1909.06737 (2019) - 2018
- [j17]Jung Hee Cheon, Duhyeong Kim, Yongdai Kim, Yongsoo Song:
Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption. IEEE Access 6: 46938-46948 (2018) - [i3]Yongdai Kim, Dongha Kim:
On variation of gradients of deep neural networks. CoRR abs/1812.00308 (2018) - [i2]Yongdai Kim, Ilsang Ohn, Dongha Kim:
Fast convergence rates of deep neural networks for classification. CoRR abs/1812.03599 (2018) - [i1]Jong-June Jeon, Yongdai Kim, Sungho Won, Hosik Choi:
Primal path algorithm for compositional data analysis. CoRR abs/1812.08954 (2018) - 2017
- [j16]Hosik Choi, Yongdai Kim, Sunghoon Kwon, Changyi Park:
A robust support vector machine for labeling errors. Commun. Stat. Simul. Comput. 46(8): 6061-6073 (2017) - 2016
- [j15]Sangin Lee, Sunghoon Kwon, Yongdai Kim:
A modified local quadratic approximation algorithm for penalized optimization problems. Comput. Stat. Data Anal. 94: 275-286 (2016) - [j14]Heng Lian, Yongdai Kim:
Nonconvex penalized reduced rank regression and its oracle properties in high dimensions. J. Multivar. Anal. 143: 383-393 (2016) - [c4]Yongdai Kim, Minwoo Chae, Kuhwan Jeong, Byungyup Kang, Hyoju Chung:
An Online Gibbs Sampler Algorithm for Hierarchical Dirichlet Processes Prior. ECML/PKDD (1) 2016: 509-523 - 2015
- [j13]Sunghoon Kwon, Sangin Lee, Yongdai Kim:
Moderately clipped LASSO. Comput. Stat. Data Anal. 92: 53-67 (2015) - [e1]Moonis Ali, Young Sig Kwon, Chang-Hwan Lee, Juntae Kim, Yongdai Kim:
Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. Lecture Notes in Computer Science 9101, Springer 2015, ISBN 978-3-319-19065-5 [contents] - 2013
- [j12]Yongdai Kim, Joungyoun Kim, Woncheol Jang:
An EM algorithm for the proportional hazards model with doubly censored data. Comput. Stat. Data Anal. 57(1): 41-51 (2013) - 2012
- [j11]Yongdai Kim, Sunghoon Kwon, Hosik Choi:
Consistent Model Selection Criteria on High Dimensions. J. Mach. Learn. Res. 13: 1037-1057 (2012) - 2011
- [j10]Sunghoon Kwon, Hosik Choi, Yongdai Kim:
Quadratic approximation on SCAD penalized estimation. Comput. Stat. Data Anal. 55(1): 421-428 (2011) - [j9]Hosik Choi, Donghwa Yeo, Sunghoon Kwon, Yongdai Kim:
Gene selection and prediction for cancer classification using support vector machines with a reject option. Comput. Stat. Data Anal. 55(5): 1897-1908 (2011) - 2010
- [j8]Yongdai Kim, Bumsoo Kim, Woncheol Jang:
Asymptotic properties of the maximum likelihood estimator for the proportional hazards model with doubly censored data. J. Multivar. Anal. 101(6): 1339-1351 (2010)
2000 – 2009
- 2009
- [j7]Hee-Seok Oh, Donghoh Kim, Yongdai Kim:
Robust wavelet shrinkage using robust selection of thresholds. Stat. Comput. 19(1): 27-34 (2009) - 2007
- [j6]Kwangkeun Yi, Hosik Choi, Jaehwang Kim, Yongdai Kim:
An empirical study on classification methods for alarms from a bug-finding static C analyzer. Inf. Process. Lett. 102(2-3): 118-123 (2007) - 2006
- [j5]Jinseog Kim, Yongdai Kim:
Maximum a posteriori pruning on decision trees and its application to bootstrap BUMPing. Comput. Stat. Data Anal. 50(3): 710-719 (2006) - [j4]Yongdai Kim, Sunghoon Kwon, Seuck Heun Song:
Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data. Comput. Stat. Data Anal. 51(3): 1643-1655 (2006) - [j3]Youjip Won, Hyungkyu Chang, Jaemin Ryu, Yongdai Kim, Junseok Shim:
Intelligent storage: Cross-layer optimization for soft real-time workload. ACM Trans. Storage 2(3): 255-282 (2006) - 2004
- [j2]Jaeyong Lee, Yongdai Kim:
A new algorithm to generate beta processes. Comput. Stat. Data Anal. 47(3): 441-453 (2004) - [j1]Yongdai Kim, Jinseog Kim:
Convex Hull Ensemble Machine for Regression and Classification. Knowl. Inf. Syst. 6(6): 645-663 (2004) - [c3]Yongdai Kim, Jinseog Kim:
Gradient LASSO for feature selection. ICML 2004 - 2003
- [c2]Yongdai Kim:
Averaged Boosting: A Noise-Robust Ensemble Method. PAKDD 2003: 388-393 - 2002
- [c1]Yongdai Kim:
Convex Hull Ensemble Machine. ICDM 2002: 243-249
Coauthor Index
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