Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. - Youngwon Choi; Yongchan Kwon; Han-Byul Lee; Beomjoon Kim; Myunghee Cho Paik; Joong-Ho Won (BrainLes@MICCAI, 2016)

Generalized estimating equations with stabilized working correlation structure. - Yongchan Kwon; Young-Geun Choi; Taesung Park; Andreas Ziegler 001; Myunghee Cho Paik (Comput. Stat. Data Anal., 2017)

An analytic formulation for positive-unlabeled learning via weighted integral probability metric. - Yongchan Kwon; Wonyoung Kim; Masashi Sugiyama; Myunghee Cho Paik (CoRR, 2019)

Uncertainty quantification of molecular property prediction with Bayesian neural networks. - Seongok Ryu; Yongchan Kwon; Woo Youn Kim (CoRR, 2019)

Uncertainty quantification of molecular property prediction using Bayesian neural network models. - Seongok Ryu; Yongchan Kwon; Woo Youn Kim (CoRR, 2019)

Valid oversampling schemes to handle imbalance. - Young-geun Kim 002; Yongchan Kwon; Myunghee Cho Paik (Pattern Recognit. Lett., 2019)

Lipschitz Continuous Autoencoders in Application to Anomaly Detection. - Young-geun Kim 002; Yongchan Kwon; Hyunwoong Chang; Myunghee Cho Paik (AISTATS, 2020)

Principled learning method for Wasserstein distributionally robust optimization with local perturbations. - Yongchan Kwon; Wonyoung Kim; Joong-Ho Won; Myunghee Cho Paik (ICML, 2020)

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. - Yongchan Kwon; Joong-Ho Won; Beomjoon Kim; Myunghee Cho Paik (Comput. Stat. Data Anal., 2020)

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks. - Doyeong Hwang; Soojung Yang; Yongchan Kwon; Kyung-Hoon Lee; Grace Lee; Hanseok Jo; Seyeol Yoon; Seongok Ryu (J. Chem. Inf. Model., 2020)

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. - Yongchan Kwon; Wonyoung Kim; Masashi Sugiyama; Myunghee Cho Paik (Mach. Learn., 2020)

Competing AI: How does competition feedback affect machine learning? - Tony Ginart; Eva Zhang; Yongchan Kwon; James Zou 001 (AISTATS, 2021)

Efficient Computation and Analysis of Distributional Shapley Values. - Yongchan Kwon; Manuel A. Rivas; James Zou 001 (AISTATS, 2021)

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. - Yongchan Kwon; James Zou 001 (AISTATS, 2022)

WeightedSHAP: analyzing and improving Shapley based feature attributions. - Yongchan Kwon; James Y. Zou (NeurIPS, 2022)

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. - Weixin Liang; Yuhui Zhang; Yongchan Kwon; Serena Yeung; James Y. Zou (NeurIPS, 2022)

Competition over data: how does data purchase affect users? - Yongchan Kwon; Tony Ginart; James Zou 001 (Trans. Mach. Learn. Res., 2022)

Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. - Yongchan Kwon; James Zou 001 (ICML, 2023)

Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. - Weixin Liang; Yining Mao; Yongchan Kwon; Xinyu Yang 002; James Zou 001 (ICML, 2023)

OpenDataVal: a Unified Benchmark for Data Valuation. - Kevin Fu Jiang; Weixin Liang; James Y. Zou; Yongchan Kwon (NeurIPS, 2023)

Data Acquisition: A New Frontier in Data-centric AI. - Lingjiao Chen; Bilge Acun; Newsha Ardalani; Yifan Sun 010; Feiyang Kang; Hanrui Lyu; Yongchan Kwon; Ruoxi Jia 001; Carole-Jean Wu; Matei Zaharia; James Zou 001 (CoRR, 2023)

DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. - Yongchan Kwon; Eric Wu; Kevin Wu; James Zou 001 (ICLR, 2024)

Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. - Jiachen T. Wang; Tianji Yang; James Zou 001; Yongchan Kwon; Ruoxi Jia 001 (ICML, 2024)

2D-OOB: Attributing Data Contribution Through Joint Valuation Framework. - Yifan Sun 010; Jingyan Shen; Yongchan Kwon (NeurIPS, 2024)

Truthful Dataset Valuation by Pointwise Mutual Information. - Shuran Zheng; Yongchan Kwon; Xuan Qi; James Zou 001 (CoRR, 2024)

Group Shapley Value and Counterfactual Simulations in a Structural Model. - Yongchan Kwon; Sokbae Lee; Guillaume A. Pouliot (CoRR, 2024)

A Survey on Data Markets. - Jiayao Zhang 006; Yuran Bi; Mengye Cheng; Jinfei Liu; Kui Ren 001; Qiheng Sun; Yihang Wu; Yang Cao 011; Raul Castro Fernandez; Haifeng Xu; Ruoxi Jia 001; Yongchan Kwon; et al. (CoRR, 2024)

Understanding Impact of Human Feedback via Influence Functions. - Taywon Min; Haeone Lee; Yongchan Kwon; Kimin Lee (ACL (1), 2025)

TimeInf: Time Series Data Contribution via Influence Functions. - Yizi Zhang; Jingyan Shen; Xiaoxue Xiong; Yongchan Kwon (ICLR, 2025)

Understanding Impact of Human Feedback via Influence Functions. - Taywon Min; Haeone Lee; Hanho Ryu; Yongchan Kwon; Kimin Lee (CoRR, 2025)

Certified Data Removal Under High-dimensional Settings. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)

Newfluence: Boosting Model interpretability and Understanding in High Dimensions. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)