Yongchan Kwon

Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)

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Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)

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Efficient Computation and Analysis of Distributional Shapley Values (AISTATS 2021)

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DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models (ICLR 2024)

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Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value (ICML 2023)

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Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)

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