Yongchan Kwon

According to our database¹, Yongchan Kwon authored at least 27 papers between 2016 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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On csauthors.net:

Bibliography

2024

Group Shapley Value and Counterfactual Simulations in a Structural Model.

[BibT_eX]

[DOI]

Yongchan Kwon

Sokbae Lee

Guillaume A. Pouliot

CoRR, 2024

2D-OOB: Attributing Data Contribution through Joint Valuation Framework.

[BibT_eX]

[DOI]

Yifan Sun

Jingyan Shen

Yongchan Kwon

CoRR, 2024

TimeInf: Time Series Data Contribution via Influence Functions.

[BibT_eX]

[DOI]

CoRR, 2024

Truthful Dataset Valuation by Pointwise Mutual Information.

[BibT_eX]

[DOI]

CoRR, 2024

Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Data Acquisition: A New Frontier in Data-centric AI.

[BibT_eX]

[DOI]

CoRR, 2023

OpenDataVal: a Unified Benchmark for Data Valuation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

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

[BibT_eX]

[DOI]

Yongchan Kwon

James Zou

Proceedings of the International Conference on Machine Learning, 2023

2022

Competition over data: how does data purchase affect users?

[BibT_eX]

[DOI]

Yongchan Kwon

Tony Ginart

James Zou

Trans. Mach. Learn. Res., 2022

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

WeightedSHAP: analyzing and improving Shapley based feature attributions.

[BibT_eX]

[DOI]

Yongchan Kwon

James Y. Zou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

[BibT_eX]

[DOI]

Yongchan Kwon

James Zou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Efficient Computation and Analysis of Distributional Shapley Values.

[BibT_eX]

[DOI]

Yongchan Kwon

Manuel A. Rivas

James Zou

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Competing AI: How does competition feedback affect machine learning?

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric.

[BibT_eX]

[DOI]

Mach. Learn., 2020

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2020

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

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2020

Principled learning method for Wasserstein distributionally robust optimization with local perturbations.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Lipschitz Continuous Autoencoders in Application to Anomaly Detection.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Valid oversampling schemes to handle imbalance.

[BibT_eX]

[DOI]

Young-geun Kim

Yongchan Kwon

Myunghee Cho Paik

Pattern Recognit. Lett., 2019

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

[BibT_eX]

[DOI]

Seongok Ryu

Yongchan Kwon

Woo Youn Kim

CoRR, 2019

Uncertainty quantification of molecular property prediction with Bayesian neural networks.

[BibT_eX]

[DOI]

Seongok Ryu

Yongchan Kwon

Woo Youn Kim

CoRR, 2019

An analytic formulation for positive-unlabeled learning via weighted integral probability metric.

[BibT_eX]

[DOI]

CoRR, 2019

2017

Generalized estimating equations with stabilized working correlation structure.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2017

2016

Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke.

[BibT_eX]

[DOI]

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2016

Yongchan Kwon

Timeline

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