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Yongchan Kwon
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2020 – today
- 2024
- [c13]Yongchan Kwon, Eric Wu, Kevin Wu, James Zou:
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. ICLR 2024 - [c12]Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia:
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. ICML 2024 - [i19]Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia:
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits. CoRR abs/2405.03875 (2024) - [i18]Shuran Zheng, Yongchan Kwon, Xuan Qi, James Zou:
Truthful Dataset Valuation by Pointwise Mutual Information. CoRR abs/2405.18253 (2024) - [i17]Yizi Zhang, Jingyan Shen, Xiaoxue Xiong, Yongchan Kwon:
TimeInf: Time Series Data Contribution via Influence Functions. CoRR abs/2407.15247 (2024) - [i16]Yifan Sun, Jingyan Shen, Yongchan Kwon:
2D-OOB: Attributing Data Contribution through Joint Valuation Framework. CoRR abs/2408.03572 (2024) - [i15]Yongchan Kwon, Sokbae Lee, Guillaume A. Pouliot:
Group Shapley Value and Counterfactual Simulations in a Structural Model. CoRR abs/2410.06875 (2024) - 2023
- [c11]Yongchan Kwon, James Zou:
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. ICML 2023: 18135-18152 - [c10]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: 20706-20724 - [c9]Kevin Fu Jiang, Weixin Liang, James Y. Zou, Yongchan Kwon:
OpenDataVal: a Unified Benchmark for Data Valuation. NeurIPS 2023 - [i14]Yongchan Kwon, James Zou:
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. CoRR abs/2304.07718 (2023) - [i13]Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou:
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. CoRR abs/2305.02995 (2023) - [i12]Kevin Fu Jiang, Weixin Liang, James Zou, Yongchan Kwon:
OpenDataVal: a Unified Benchmark for Data Valuation. CoRR abs/2306.10577 (2023) - [i11]Yongchan Kwon, Eric Wu, Kevin Wu, James Zou:
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models. CoRR abs/2310.00902 (2023) - [i10]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 abs/2311.13712 (2023) - 2022
- [j6]Yongchan Kwon, Tony Ginart, James Zou:
Competition over data: how does data purchase affect users? Trans. Mach. Learn. Res. 2022 (2022) - [c8]Yongchan Kwon, James Zou:
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. AISTATS 2022: 8780-8802 - [c7]Yongchan Kwon, James Y. Zou:
WeightedSHAP: analyzing and improving Shapley based feature attributions. NeurIPS 2022 - [c6]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 - [i9]Yongchan Kwon, Antonio Ginart, James Zou:
Competition over data: how does data purchase affect users? CoRR abs/2201.10774 (2022) - [i8]Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, James Zou:
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning. CoRR abs/2203.02053 (2022) - [i7]Yongchan Kwon, James Zou:
WeightedSHAP: analyzing and improving Shapley based feature attributions. CoRR abs/2209.13429 (2022) - 2021
- [c5]Yongchan Kwon, Manuel A. Rivas, James Zou:
Efficient Computation and Analysis of Distributional Shapley Values. AISTATS 2021: 793-801 - [c4]Tony Ginart, Eva Zhang, Yongchan Kwon, James Zou:
Competing AI: How does competition feedback affect machine learning? AISTATS 2021: 1693-1701 - [i6]Yongchan Kwon, James Zou:
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. CoRR abs/2110.14049 (2021) - 2020
- [j5]Yongchan Kwon, Joong-Ho Won, Beomjoon Kim
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation. Comput. Stat. Data Anal. 142 (2020) - [j4]Doyeong Hwang, Soojung Yang
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks. J. Chem. Inf. Model. 60(12): 5936-5945 (2020) - [j3]Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach. Learn. 109(3): 513-532 (2020) - [c3]Young-geun Kim, Yongchan Kwon, Hyunwoong Chang, Myunghee Cho Paik:
Lipschitz Continuous Autoencoders in Application to Anomaly Detection. AISTATS 2020: 2507-2517 - [c2]Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik:
Principled learning method for Wasserstein distributionally robust optimization with local perturbations. ICML 2020: 5567-5576 - [i5]Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik:
Principled Learning Method for Wasserstein distributionally robust optimization with local perturbations. CoRR abs/2006.03333 (2020) - [i4]Yongchan Kwon, Manuel A. Rivas, James Zou:
Efficient computation and analysis of distributional Shapley values. CoRR abs/2007.01357 (2020)
2010 – 2019
- 2019
- [j2]Young-geun Kim
Valid oversampling schemes to handle imbalance. Pattern Recognit. Lett. 125: 661-667 (2019) - [i3]Yongchan Kwon, Wonyoung Kim, Masashi Sugiyama, Myunghee Cho Paik:
An analytic formulation for positive-unlabeled learning via weighted integral probability metric. CoRR abs/1901.09503 (2019) - [i2]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction with Bayesian neural networks. CoRR abs/1903.08375 (2019) - [i1]Seongok Ryu, Yongchan Kwon, Woo Youn Kim:
Uncertainty quantification of molecular property prediction using Bayesian neural network models. CoRR abs/1905.06945 (2019) - 2017
- [j1]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: 1-11 (2017) - 2016
- [c1]Youngwon Choi
Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. BrainLes@MICCAI 2016: 231-243
Coauthor Index
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