Yongchan Kwon

52 papers A* 10A 4Journal 36Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
DSGym: A Holistic Framework for Evaluating and Training Data Science Agents.
CoRR
Fan Nie, Junlin Wang, Harper Hua, Federico Bianchi, Yongchan Kwon, Zhenting Qi, Owen Queen, Shang Zhu, James Zou
2026 J jnl
Guest Editorial: Special Issue on Visual Datasets.
Int. J. Comput. Vis.
Xin Zhao, Liang Zheng, Qiang Qiu, Yin Li, Limin Wang, José Lezama, Qiuhong Ke, Yongchan Kwon, Ruoxi Jia, Jungong Han
2026 J jnl
What LLMs Think When You Don't Tell Them What to Think About?
CoRR
Yongchan Kwon, James Zou
2025 J jnl
Certified Data Removal Under High-dimensional Settings.
CoRR
Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki
2025 J jnl
Certified Machine Unlearning Under High Dimensional Regime.
J. Mach. Learn. Res.
Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki
2025 J jnl
Data Acquisition: A New Frontier in Data-centric AI.
J. Data-centric Mach. Learn. Res.
Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
2025 J jnl
Newfluence: Boosting Model interpretability and Understanding in High Dimensions.
CoRR
Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki
2025 J jnl
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning.
CoRR
Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, James Zou
2025 A* conf
TimeInf: Time Series Data Contribution via Influence Functions.
ICLR
Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon
2025 J jnl
To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis.
CoRR
Federico Bianchi, Yongchan Kwon, Zachary Izzo, Linjun Zhang, James Zou
2025 — conf
Understanding Impact of Human Feedback via Influence Functions.
ACL (1)
Taywon Min, Haeone Lee, Yongchan Kwon, Kimin Lee
2025 J jnl
Understanding Impact of Human Feedback via Influence Functions.
CoRR
Taywon Min, Haeone Lee, Hanho Ryu, Yongchan Kwon, Kimin Lee
2024 A* conf
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework.
NeurIPS
Yifan Sun, Jingyan Shen, Yongchan Kwon
2024 J jnl
2D-OOB: Attributing Data Contribution through Joint Valuation Framework.
CoRR
Yifan Sun, Jingyan Shen, Yongchan Kwon
2024 J jnl
A Survey on Data Markets.
CoRR
Jiayao Zhang, Yuran Bi, Mengye Cheng, Jinfei Liu, Kui Ren, Qiheng Sun, Yihang Wu, Yang Cao, Raul Castro Fernandez, Haifeng Xu, Ruoxi Jia, Yongchan Kwon, Jian Pei, Jiachen T. Wang, Haocheng Xia, Li Xiong, Xiaohui Yu, James Zou
2024 A* conf
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.
ICLR
Yongchan Kwon, Eric Wu, Kevin Wu, James Zou
2024 J jnl
Group Shapley Value and Counterfactual Simulations in a Structural Model.
CoRR
Yongchan Kwon, Sokbae Lee, Guillaume A. Pouliot
2024 A* conf
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits.
ICML
Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia
2024 J jnl
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits.
CoRR
Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia
2024 J jnl
TimeInf: Time Series Data Contribution via Influence Functions.
CoRR
Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon
2024 J jnl
Truthful Dataset Valuation by Pointwise Mutual Information.
CoRR
Shuran Zheng, Yongchan Kwon, Xuan Qi, James Zou
2023 A* conf
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations.
ICML
Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou
2023 J jnl
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations.
CoRR
Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou
2023 J jnl
Data Acquisition: A New Frontier in Data-centric AI.
CoRR
Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
2023 A* conf
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value.
ICML
Yongchan Kwon, James Zou
2023 J jnl
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value.
CoRR
Yongchan Kwon, James Zou
2023 J jnl
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.
CoRR
Yongchan Kwon, Eric Wu, Kevin Wu, James Zou
2023 A* conf
OpenDataVal: a Unified Benchmark for Data Valuation.
NeurIPS
Kevin Fu Jiang, Weixin Liang, James Y. Zou, Yongchan Kwon
2023 J jnl
OpenDataVal: a Unified Benchmark for Data Valuation.
CoRR
Kevin Fu Jiang, Weixin Liang, James Zou, Yongchan Kwon
2022 A conf
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning.
AISTATS
Yongchan Kwon, James Zou
2022 J jnl
Competition over data: how does data purchase affect users?
CoRR
Yongchan Kwon, Antonio Ginart, James Zou
2022 J jnl
Competition over data: how does data purchase affect users?
Trans. Mach. Learn. Res.
Yongchan Kwon, Tony Ginart, James Zou
2022 A* conf
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning.
NeurIPS
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Y. Zou
2022 J jnl
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning.
CoRR
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Zou
2022 A* conf
WeightedSHAP: analyzing and improving Shapley based feature attributions.
NeurIPS
Yongchan Kwon, James Y. Zou
2022 J jnl
WeightedSHAP: analyzing and improving Shapley based feature attributions.
CoRR
Yongchan Kwon, James Zou
2021 J jnl
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning.
CoRR
Yongchan Kwon, James Zou
2021 A conf
Competing AI: How does competition feedback affect machine learning?
AISTATS
Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou
2021 A conf
Efficient Computation and Analysis of Distributional Shapley Values.
AISTATS
Yongchan Kwon, Manuel A. Rivas, James Zou
2020 J jnl
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.
J. Chem. Inf. Model.
Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung-Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu
2020 J jnl
Efficient computation and analysis of distributional Shapley values.
CoRR
Yongchan Kwon, Manuel A. Rivas, James Zou
2020 A conf
Lipschitz Continuous Autoencoders in Application to Anomaly Detection.
AISTATS
Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik
2020 J jnl
Principled Learning Method for Wasserstein distributionally robust optimization with local perturbations.
CoRR
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik
2020 J jnl
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric.
Mach. Learn.
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
2020 A* conf
Principled learning method for Wasserstein distributionally robust optimization with local perturbations.
ICML
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik
2020 J jnl
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation.
Comput. Stat. Data Anal.
Yongchan Kwon, Joong-Ho Won, Beomjoon Kim, Myunghee Cho Paik
2019 J jnl
An analytic formulation for positive-unlabeled learning via weighted integral probability metric.
CoRR
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
2019 J jnl
Uncertainty quantification of molecular property prediction using Bayesian neural network models.
CoRR
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
2019 J jnl
Uncertainty quantification of molecular property prediction with Bayesian neural networks.
CoRR
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
2019 J jnl
Valid oversampling schemes to handle imbalance.
Pattern Recognit. Lett.
Young-geun Kim, Yongchan Kwon, Myunghee Cho Paik
2017 J jnl
Generalized estimating equations with stabilized working correlation structure.
Comput. Stat. Data Anal.
Yongchan Kwon, Young-Geun Choi, Taesung Park, Andreas Ziegler, Myunghee Cho Paik
2016 — conf
Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke.
BrainLes@MICCAI
Youngwon Choi, Yongchan Kwon, Han-Byul Lee, Beomjoon Kim, Myunghee Cho Paik, Joong-Ho Won