Yongchan Kwon

According to our database1, Yongchan Kwon authored at least 22 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Data Acquisition: A New Frontier in Data-centric AI.
CoRR, 2023

DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.
CoRR, 2023

OpenDataVal: a Unified Benchmark for Data Valuation.
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.
Proceedings of the International Conference on Machine Learning, 2023

Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value.
Proceedings of the International Conference on Machine Learning, 2023

2022
Competition over data: how does data purchase affect users?
Trans. Mach. Learn. Res., 2022

Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning.
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.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Efficient Computation and Analysis of Distributional Shapley Values.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Competing AI: How does competition feedback affect machine learning?
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.
Mach. Learn., 2020

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.
J. Chem. Inf. Model., 2020

Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation.
Comput. Stat. Data Anal., 2020

Principled learning method for Wasserstein distributionally robust optimization with local perturbations.
Proceedings of the 37th International Conference on Machine Learning, 2020

Lipschitz Continuous Autoencoders in Application to Anomaly Detection.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Valid oversampling schemes to handle imbalance.
Pattern Recognit. Lett., 2019

Uncertainty quantification of molecular property prediction using Bayesian neural network models.
CoRR, 2019

Uncertainty quantification of molecular property prediction with Bayesian neural networks.
CoRR, 2019

An analytic formulation for positive-unlabeled learning via weighted integral probability metric.
CoRR, 2019

2017
Generalized estimating equations with stabilized working correlation structure.
Comput. Stat. Data Anal., 2017

2016
Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2016


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