Yongdai Kim
Orcid: 0000-0002-9434-5645
According to our database1,
Yongdai Kim
authored at least 50 papers
between 2002 and 2024.
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Bibliography
2024
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights.
CoRR, 2024
IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
<i>L</i><sub><i>q</i></sub> regularization for fair artificial intelligence robust to covariate shift.
Stat. Anal. Data Min., June, 2023
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models.
J. Mach. Learn. Res., 2023
Online learning for the Dirichlet process mixture model via weakly conjugate approximation.
Comput. Stat. Data Anal., 2023
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models.
CoRR, 2023
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples.
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
2022
Stat. Anal. Data Min., 2022
Neural Networks, 2022
Neural Comput., 2022
Proceedings of the International Conference on Machine Learning, 2022
2021
Neural Networks, 2021
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks.
CoRR, 2021
Understanding Effects of Architecture Design to Invariance and Complexity in Deep Neural Networks.
IEEE Access, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Comput. Stat. Data Anal., 2020
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020
2019
Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market.
Ind. Manag. Data Syst., 2019
Smooth Function Approximation by Deep Neural Networks with General Activation Functions.
Entropy, 2019
2018
Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption.
IEEE Access, 2018
2017
Commun. Stat. Simul. Comput., 2017
2016
Nonconvex penalized reduced rank regression and its oracle properties in high dimensions.
J. Multivar. Anal., 2016
A modified local quadratic approximation algorithm for penalized optimization problems.
Comput. Stat. Data Anal., 2016
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016
2015
2013
Comput. Stat. Data Anal., 2013
2012
2011
Gene selection and prediction for cancer classification using support vector machines with a reject option.
Comput. Stat. Data Anal., 2011
2010
Asymptotic properties of the maximum likelihood estimator for the proportional hazards model with doubly censored data.
J. Multivar. Anal., 2010
2009
2007
An empirical study on classification methods for alarms from a bug-finding static C analyzer.
Inf. Process. Lett., 2007
2006
ACM Trans. Storage, 2006
Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data.
Comput. Stat. Data Anal., 2006
Maximum a posteriori pruning on decision trees and its application to bootstrap BUMPing.
Comput. Stat. Data Anal., 2006
2004
Knowl. Inf. Syst., 2004
2003
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2003
2002
Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002