Yongchan Kwon

发表

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI

Andrew L Beers, J. Pauly, Joong-Ho Won, 2018, Front. Neurol..

A robust calibration-assisted method for linear mixed effects model under cluster-specific nonignorable missingness

Jae Kwang Kim, M. Paik, Yongchan Kwon, 2018 .

Competition over data: how does data purchase affect users?

Antonio A. Ginart, James Y. Zou, Yongchan Kwon, 2022, ArXiv.

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Omar E. Cornejo, Christopher M. DeBoever, Manuel A. Rivas, 2022, Nature Communications.

WeightedSHAP: analyzing and improving Shapley based feature attributions

Yongchan Kwon, James Y. Zou, 2022, NeurIPS.

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning

James Y. Zou, S. Yeung, Weixin Liang, 2022, NeurIPS.

Efficient computation and analysis of distributional Shapley values

James Zou, Manuel A. Rivas, Yongchan Kwon, 2020, AISTATS.

Competing AI: How does competition feedback affect machine learning?

Yongchan Kwon, James Zou, Antonio Ginart, 2020, AISTATS.

A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

Seongok Ryu, Woo Youn Kim, Yongchan Kwon, 2019, Chemical science.

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning pdf

Yongchan Kwon, James Y. Zou, James Zou, 2021, AISTATS.

Generalized estimating equations with stabilized working correlation structure

Taesung Park, Young-Geun Choi, Andreas Ziegler, 2017, Comput. Stat. Data Anal..

Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

James Y. Zou, Yongchan Kwon, 2023, ArXiv.

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation

Myunghee Cho Paik, Yongchan Kwon, Joong Ho Won, 2020, Comput. Stat. Data Anal..

Uncertainty quantification of molecular property prediction with Bayesian neural networks pdf

Seongok Ryu, Woo Youn Kim, Yongchan Kwon, 2019, ArXiv.

Uncertainty quantification of molecular property prediction using Bayesian neural network models pdf

Seongok Ryu, Woo Youn Kim, Yongchan Kwon, 2018, ArXiv.

Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value

James Y. Zou, Yongchan Kwon, 2023, ICML.

On the nonlinear correlation of ML performance between data subpopulations

James Y. Zou, Weixin Liang, Yongchan Kwon, 2023, arXiv.org.

Lipschitz Continuous Autoencoders in Application to Anomaly Detection

Yongchan Kwon, Young-geun Kim, Myunghee Cho Paik, 2020, AISTATS.

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks

Seongok Ryu, Grace Lee, Kyung Hoon Lee, 2020, J. Chem. Inf. Model..

Principled Learning Method for Wasserstein distributionally robust optimization with local perturbations

Joong-Ho Won, Yongchan Kwon, Myunghee Cho Paik, 2020, ICML.

Valid oversampling schemes to handle imbalance

Myunghee Cho Paik, Yongchan Kwon, Younggeun Kim, 2019, Pattern Recognit. Lett..

Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation

Myunghee Cho Paik, Joong-Ho Won, Yongchan Kwon, 2018 .

Prior Elicitation Methods in the Normal Linear Model

권용찬, Yongchan Kwon, 2013 .

Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke

Joong-Ho Won, Han-Byul Lee, Beomjoon Kim, 2016, BrainLes@MICCAI.

OpenDataVal: a Unified Benchmark for Data Valuation

James Y. Zou, Weixin Liang, Yongchan Kwon, 2023, ArXiv.

DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Yongchan Kwon, K. Wu, Eric Wu, 2023, ArXiv.