Kwon, Yongchan

20 publications

DMLR 2025 Data Acquisition: A New Frontier in Data-Centric AI Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou

ICLRW 2025 Proper Dataset Valuation by Pointwise Mutual Information Shuran Zheng, Xuan Qi, Rui Ray Chen, Yongchan Kwon, James Zou

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

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

ICLR 2024 DataInf: Efficiently Estimating Data Influence in LoRA-Tuned LLMs and Diffusion Models Yongchan Kwon, Eric Wu, Kevin Wu, James Zou

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

ICML 2023 Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou

ICML 2023 Data-OOB: Out-of-Bag Estimate as a Simple and Efficient Data Value Yongchan Kwon, James Zou

NeurIPS 2023 OpenDataVal: A Unified Benchmark for Data Valuation Kevin Jiang, Weixin Liang, James Y Zou, Yongchan Kwon

AISTATS 2022 Beta Shapley: A Unified and Noise-Reduced Data Valuation Framework for Machine Learning Yongchan Kwon, James Zou

TMLR 2022 Competition over Data: How Does Data Purchase Affect Users? Yongchan Kwon, Tony A Ginart, James Zou

NeurIPS 2022 Mind the Gap: Understanding the Modality Gap in Multi-Modal Contrastive Representation Learning Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Y Zou

ICMLW 2022 Mind the Gap: Understanding the Modality Gap in Multi-Modal Contrastive Representation Learning Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Zou

ICMLW 2022 On the Nonlinear Correlation of ML Performance Across Data Subpopulations Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou

NeurIPS 2022 WeightedSHAP: Analyzing and Improving Shapley Based Feature Attributions Yongchan Kwon, James Y Zou

AISTATS 2021 Competing AI: How Does Competition Feedback Affect Machine Learning? Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou

AISTATS 2021 Efficient Computation and Analysis of Distributional Shapley Values Yongchan Kwon, Manuel A. Rivas, James Zou

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

MLJ 2020 Principled Analytic Classifier for Positive-Unlabeled Learning via Weighted Integral Probability Metric Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik

ICML 2020 Principled Learning Method for Wasserstein Distributionally Robust Optimization with Local Perturbations Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik