Publications

An up-to-date list can be found at Google Scholar.

• Kwon, Y.*, Wu, E.*, Wu, K.*, and Zou, J. (2024). DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. International Conference on Learning Representations. (ICLR 2024). [URL]. [GitHub].

• Jiang, K.*, Liang, W.*, Zou, J. and Kwon, Y. (2023). OpenDataVal: a Unified Benchmark for Data Valuation. Advances in Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks. [URL]. [Website]. [GitHub].

• Kwon, Y. and Zou, J. (2023). Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. International Conference on Machine Learning (ICML 2023). [URL]. [GitHub].

• Liang, W.*, Mao, Y.*, Kwon, Y.*, Yang, X., and Zou, J. (2023). On the nonlinear correlation of ML performance between data subpopulations. International Conference on Machine Learning (ICML 2023). [URL]. [Website].

• Kwon, Y., Ginart, T., and Zou, J. (2022). Competition over data: how does data purchase affect users? Transactions of Machine Learning Research (TMLR). [URL]. [GitHub].

• Kwon, Y. and Zou, J. (2022). WeightedSHAP: analyzing and improving Shapley based feature attributions. Neural Information Processing Systems (NeurIPS 2022). [URL]. [GitHub].

• Liang, W.*, Zhang, Y.*, Kwon, Y.*, Yeung, S., and Zou, J. (2022). Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. Neural Information Processing Systems (NeurIPS 2022). [URL]. [Website]. [GitHub].

• Parikh. V., …, Kwon, Y., …, and Ashley, E. (2022). Deconvoluting complex correlates of COVID19 severity with a multi-omic pandemic tracking strategy. Nature Communications. 13(1), 1-10. [URL].

• Kwon, Y. and Zou, J.. (2022). Beta-Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), PMLR 151:8780-8802. [URL]. [GitHub]. (selected for oral presentation, Top 2.6%).

• Kwon, Y., Rivas, M.A., and Zou, J.. (2021). Efficient computation and analysis of distributional Shapley values. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), PMLR 130:793-801. [URL]. [GitHub].

• Ginart, A.A., Zhang, E., Kwon, Y., and Zou, J.. (2021). Competing AI: How does competition feedback affect machine learning. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), PMLR 130:1693-1701. [URL]. [GitHub]. [Press].

• Hwang, D., Yang, S., Kwon, Y., Lee, K., Lee, G., Jo, H., Yoon, S., and Ryu, S.. (2020). A comprehensive study on molecular supervised learning with graph neural networks. Journal of Chemical Information and Modeling: 5936-5945. [URL]. [GitHub].

• Kwon, Y., Kim, W., Won, J.-H., and Paik, M.C.. (2020). Principled learning method for Wasserstein distributionally robust optimization with local perturbations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119:5567-5576. [URL]. [GitHub].

• Kim, Y., Kwon, Y., Chang, H., and Paik, M.C.. (2020). Lipschitz Continuous Autoencoders in Application to Anomaly Detection. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), PMLR 108:2507-2517. [URL]. [GitHub].

• Kwon, Y., Kim, W., Sugiyama, M., and Paik, M.C.. (2020). Principled analytic formulation for positive-unlabeled learning via weighted integral probability metric. Machine Learning, 109 (3): 513-532. [URL]. [GitHub].

• Kwon, Y., Won, J.-H., Kim, B. J. and Paik, M.C.. (2020). Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Computational Statistics & Data Analysis, 142: 106816. [URL]. [GitHub].
  • Conference version is accepted as an Oral presentation at International Conference on Medical Imaging with Deep Learning 2018 (MIDL 2018).

• Ryu, S., Kwon, Y., and Kim, W.. (2019). Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification. Chemical Science, 10 (36): 8438-8446. [URL]. [GitHub].

• Kim, Y., Kwon, Y., and Paik, M.C.. (2019). Valid oversampling schemes to handle imbalance. Pattern Recognition Letters, 125 (1): 661-667. [URL]. [GitHub].

• Kwon, Y., Kim, J., Paik, M.C., and Kim, H.. (2018). A robust calibration-assisted method for linear mixed effects model under cluster-specific nonignorable missingness. Statistica Sinica, 28 (4): 1907-1928. [URL].

• Winzeck S, …, Kwon, Y., et al. (2018). ISLES 2016 & 2017. Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI. Frontiers in Neurology, 9, 679. [URL].

• Choi, Y.*, Kwon, Y.*, Lee, H.*, Kim, B. J., Paik, M.C., and Won, J.-H.. (2017). Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science, 10154: 231-243. [URL].

• Kwon, Y., Choi, Y.-G., Park, T., Ziegler, A., and Paik, M.C.. (2017). Generalized estimating equations with stabilized working correlation structure. Computational Statistics & Data Analysis, 106: 1-11. [URL].

• Kim, J., Kwon, Y., Paik, M.C.. (2016). Calibrated propensity score method for survey nonresponse in cluster sampling. Biometrika, 103 (2): 461-473. [URL].

* indicates the authors contributed equally.