| Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation Y Kwon, JH Won, BJ Kim, MC Paik Computational Statistics & Data Analysis 142, 106816, 2020 | 435* | 2020 |
| Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning W Liang, Y Zhang, Y Kwon, S Yeung, J Zou Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 188 | 2022 |
| ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in neurology 9, 679, 2018 | 166 | 2018 |
| A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification S Ryu, Y Kwon, WY Kim Chemical science 10 (36), 8438-8446, 2019 | 141* | 2019 |
| Beta shapley: a unified and noise-reduced data valuation framework for machine learning Y Kwon, J Zou International Conference on Artificial Intelligence and Statistics, 8780-8802, 2022 | 71 | 2022 |
| Ensemble of deep convolutional neural networks for prognosis of ischemic stroke Y Choi, Y Kwon, H Lee, BJ Kim, MC Paik, JH Won International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke …, 2017 | 59 | 2017 |
| Efficient computation and analysis of distributional Shapley values Y Kwon, MA Rivas, J Zou International Conference on Artificial Intelligence and Statistics, 793-801, 2021 | 57 | 2021 |
| Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks D Hwang, S Yang, Y Kwon, KH Lee, G Lee, H Jo, S Yoon, S Ryu Journal of Chemical Information and Modeling, 2020 | 25 | 2020 |
| WeightedSHAP: analyzing and improving Shapley based feature attributions Y Kwon, J Zou Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 20 | 2022 |
| Calibrated propensity score method for survey nonresponse in cluster sampling JK Kim, Y Kwon, MC Paik Biometrika 103 (2), 461-473, 2016 | 17 | 2016 |
| Valid oversampling schemes to handle imbalance Y Kim, Y Kwon, MC Paik Pattern Recognition Letters 125, 661-667, 2019 | 15 | 2019 |
| Competing AI: How does competition feedback affect machine learning A Ginart, E Zhang, Y Kwon, J Zou International Conference on Artificial Intelligence and Statistics 130, 1693 …, 2021 | 12 | 2021 |
| Principled Learning Method for Wasserstein Distributionally Robust Optimization with Local Perturbations Y Kwon, W Kim, JH Won, MC Paik International Conference on Machine Learning 119, 5567-5576, 2020 | 12 | 2020 |
| Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value Y Kwon, J Zou International Conference on Machine Learning (ICML), 2023 | 10 | 2023 |
| Lipschitz continuous autoencoders in application to anomaly detection Y Kim, Y Kwon, H Chang, MC Paik International Conference on Artificial Intelligence and Statistics, 2507-2517, 2020 | 10 | 2020 |
| Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric Y Kwon, W Kim, M Sugiyama, MC Paik Machine Learning, 1-20, 2019 | 10 | 2019 |
| Generalized estimating equations with stabilized working correlation structure Y Kwon, YG Choi, T Park, A Ziegler, MC Paik Computational statistics & data analysis 106, 1-11, 2017 | 7 | 2017 |
| Opendataval: a unified benchmark for data valuation K Jiang, W Liang, JY Zou, Y Kwon Advances in Neural Information Processing Systems 36, 2023 | 6 | 2023 |
| Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy VN Parikh, AG Ioannidis, D Jimenez-Morales, JE Gorzynski, HN De Jong, ... Nature communications 13 (1), 5107, 2022 | 6* | 2022 |
| Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations W Liang, Y Mao, Y Kwon, X Yang, J Zou International Conference on Machine Learning (ICML), 2023 | 5* | 2023 |
Myunghee Cho PaikProfessor of Statistics, Seoul National UniversityVerified email at snu.ac.kr
