| 2026 | ACL | ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning. | Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, James Zou |
| 2025 | ACL | Understanding Impact of Human Feedback via Influence Functions. | Taywon Min, Haeone Lee, Yongchan Kwon, Kimin Lee |
| 2025 | ICLR | TimeInf: Time Series Data Contribution via Influence Functions. | Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon |
| 2024 | ICLR | DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. | Yongchan Kwon, Eric Wu, Kevin Wu, James Zou |
| 2024 | ICML | Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. | Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia |
| 2023 | ICML | Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. | Yongchan Kwon, James Zou |
| 2023 | ICML | Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. | Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou |
| 2022 | AISTATS | Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. | Yongchan Kwon, James Zou |
| 2021 | AISTATS | Competing AI: How does competition feedback affect machine learning? | Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou |
| 2021 | AISTATS | Efficient Computation and Analysis of Distributional Shapley Values. | Yongchan Kwon, Manuel A. Rivas, James Zou |
| 2020 | AISTATS | Lipschitz Continuous Autoencoders in Application to Anomaly Detection. | Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik |
| 2020 | ICML | Principled learning method for Wasserstein distributionally robust optimization with local perturbations. | Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik |
| 2016 | MICCAI | 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 |