Cuong Nguyen

Orcid: 0000-0003-2672-6291

Affiliations:
  • University of Adelaide, Australian Institute for Machine Learning (AIML), Australia


According to our database1, Cuong Nguyen authored at least 17 papers between 2020 and 2024.

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

Timeline

2020
2021
2022
2023
2024
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5
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5
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Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Coverage-Constrained Human-AI Cooperation with Multiple Experts.
CoRR, 2024

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning.
CoRR, 2024

MetaAug: Meta-data Augmentation for Post-training Quantization.
Proceedings of the Computer Vision - ECCV 2024, 2024

Instance-Dependent Noisy-Label Learning with Graphical Model Based Noise-Rate Estimation.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Task Weighting in Meta-learning with Trajectory Optimisation.
Trans. Mach. Learn. Res., 2023

PAC-Bayes Meta-Learning With Implicit Task-Specific Posteriors.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

Noisy-label Learning with Sample Selection based on Noise Rate Estimate.
CoRR, 2023

PASS: Peer-Agreement based Sample Selection for training with Noisy Labels.
CoRR, 2023

Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach.
CoRR, 2023

Instance-Dependent Noisy Label Learning via Graphical Modelling.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning.
CoRR, 2022

2021
Similarity of Classification Tasks.
CoRR, 2021

Probabilistic task modelling for meta-learning.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020
PAC-Bayesian Meta-learning with Implicit Prior.
CoRR, 2020

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Unsupervised Task Design to Meta-Train Medical Image Classifiers.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020


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