Yongchan Kwon

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 35

Top Topics

Pointwise Mutual Information

Class Conditional Densities

Dempster Shafer

Top Venues

Publications

Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. CoRR (2024)
Shuran Zheng, Yongchan Kwon, Xuan Qi, James Zou
Truthful Dataset Valuation by Pointwise Mutual Information. CoRR (2024)
Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon
TimeInf: Time Series Data Contribution via Influence Functions. CoRR (2024)
Yongchan Kwon, Eric Wu, Kevin Wu, James Zou
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. ICLR (2024)
Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
Data Acquisition: A New Frontier in Data-centric AI. CoRR (2023)
Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou
On the nonlinear correlation of ML performance between data subpopulations. CoRR (2023)
Yongchan Kwon, Eric Wu, Kevin Wu, James Zou
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. CoRR (2023)
Kevin Fu Jiang, Weixin Liang, James Zou, Yongchan Kwon
OpenDataVal: a Unified Benchmark for Data Valuation. CoRR (2023)
Yongchan Kwon, James Zou
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. ICML (2023)
Kevin Fu Jiang, Weixin Liang, James Y. Zou, Yongchan Kwon
OpenDataVal: a Unified Benchmark for Data Valuation. NeurIPS (2023)
Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. ICML (2023)
Yongchan Kwon, James Zou
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. CoRR (2023)
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Y. Zou
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. NeurIPS (2022)
Yongchan Kwon, James Zou
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. AISTATS (2022)
Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Zou
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. CoRR (2022)
Yongchan Kwon, Antonio Ginart, James Zou
Competition over data: how does data purchase affect users? CoRR (2022)
Yongchan Kwon, James Y. Zou
WeightedSHAP: analyzing and improving Shapley based feature attributions. NeurIPS (2022)
Yongchan Kwon, James Zou
WeightedSHAP: analyzing and improving Shapley based feature attributions. CoRR (2022)
Yongchan Kwon, Tony Ginart, James Zou
Competition over data: how does data purchase affect users? Trans. Mach. Learn. Res. 2022 (2022)
Yongchan Kwon, James Zou
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. CoRR (2021)
Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou
Competing AI: How does competition feedback affect machine learning? AISTATS (2021)
Yongchan Kwon, Manuel A. Rivas, James Zou
Efficient Computation and Analysis of Distributional Shapley Values. AISTATS (2021)
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik
Principled learning method for Wasserstein distributionally robust optimization with local perturbations. ICML (2020)
Yongchan Kwon, Manuel A. Rivas, James Zou
Efficient computation and analysis of distributional Shapley values. CoRR (2020)
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik
Principled Learning Method for Wasserstein distributionally robust optimization with local perturbations. CoRR (2020)
Yongchan Kwon, Joong-Ho Won, Beomjoon Kim, Myunghee Cho Paik
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Comput. Stat. Data Anal. 142 (2020)
Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik
Lipschitz Continuous Autoencoders in Application to Anomaly Detection. AISTATS (2020)
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach. Learn. 109 (3) (2020)
Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung-Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks. J. Chem. Inf. Model. 60 (12) (2020)
Young-geun Kim, Yongchan Kwon, Myunghee Cho Paik
Valid oversampling schemes to handle imbalance. Pattern Recognit. Lett. 125 (2019)
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Uncertainty quantification of molecular property prediction using Bayesian neural network models. CoRR (2019)
Seongok Ryu, Yongchan Kwon, Woo Youn Kim
Uncertainty quantification of molecular property prediction with Bayesian neural networks. CoRR (2019)
Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik
An analytic formulation for positive-unlabeled learning via weighted integral probability metric. CoRR (2019)
Yongchan Kwon, Young-Geun Choi, Taesung Park, Andreas Ziegler, Myunghee Cho Paik
Generalized estimating equations with stabilized working correlation structure. Comput. Stat. Data Anal. 106 (2017)
Youngwon Choi, Yongchan Kwon, Han-Byul Lee, Beomjoon Kim, Myunghee Cho Paik, Joong-Ho Won
Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. BrainLes@MICCAI (2016)