1. Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner. Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. arXiv:2202.12275.
  2. Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho, Vu Dinh, Anh T. Tran, Tal Hassner, Cuong V. Nguyen. Transferability Between Regression Tasks. NeurIPS Workshop on Distribution Shifts: Connecting Methods and Applications, 2022.
  3. Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh, Tal Hassner, Cuong V. Nguyen. Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. International Symposium on Information Theory and Its Applications (ISITA 2022).
  4. Cuong V. Nguyen, Lam Si Tung Ho, Huan Xu, Vu Dinh, Binh T. Nguyen. Bayesian active learning with abstention feedbacks. Neurocomputing, 2022. (arxiv)
  5. Cuong V. Nguyen, Sanjiv R. Das, John He, Shenghua Yue, Vinay Hanumaiah, Xavier Ragot, Li Zhang. Multimodal machine learning for credit modeling. IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2021). (paper)
  6. Cuong V. Nguyen, Tal Hassner, Cedric Archambeau, Matthias Seeger. LEEP: A New Measure to Evaluate Transferability of Learned Representations. International Conference on Machine Learning (ICML 2020).
  7. Cuong V. Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, Stefano Soatto. Meta-analysis of Continual Learning. Workshop on Meta-Learning @ NeurIPS 2019. (old version)
  8. Anh T. Tran, Cuong V. Nguyen, Tal Hassner. Transferability and Hardness of Supervised Classification Tasks. International Conference on Computer Vision (ICCV 2019, oral).
  9. Le Thi Khanh Hien, Cuong V. Nguyen, Huan Xu, Canyi Lu, Jiashi Feng. Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization. Journal of Optimization Theory and Applications, 2019. (arxiv)
  10. Lam Si Tung Ho, Vu Dinh, Cuong V. Nguyen. Multi-task learning improves ancestral state reconstruction. Theoretical Population Biology, 2019.
  11. Quang H. Pham, Binh T. Nguyen, Nguyen Viet Cuong. Punctuation Prediction for Vietnamese Texts Using Conditional Random Fields. International Symposium on Information and Communication Technology (SoICT 2019).
  12. Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui and Richard E. Turner. Improving and Understanding Variational Continual Learning. Continual Learning Workshop @ NeurIPS 2018.
  13. Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner. Variational Continual Learning. International Conference on Learning Representations (ICLR 2018). (code)
  14. Thang D. Bui*, Cuong V. Nguyen*, Richard E. Turner. Streaming Sparse Gaussian Process Approximations. Advances in Neural Information Processing Systems (NIPS 2017). (*equal contribution) (code)
  15. Cuong V. Nguyen, Thang D. Bui, Yingzhen Li, Richard E. Turner. Online Variational Bayesian Inference: Algorithms for Sparse Gaussian Processes and Theoretical Bounds. Time Series Workshop @ ICML 2017.
  16. Nguyen Viet Cuong, Huan Xu. Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint. Advances in Neural Information Processing Systems (NIPS 2016).
  17. Nguyen Viet Cuong, Nan Ye, Wee Sun Lee. Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification. AAAI Conference on Artificial Intelligence (AAAI 2016).
  18. Nguyen Viet Cuong, Muthu Kumar Chandrasekaran, Min-Yen Kan, Wee Sun Lee. Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFs. Joint Conference on Digital Libraries (JCDL 2015).
  19. Vu Dinh, Lam Si Tung Ho, Nguyen Viet Cuong, Duy Nguyen, Binh T. Nguyen. Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers. Conference on Theory and Applications of Models of Computation (TAMC 2015).
  20. Quang H. Pham, Minh-Le Nguyen, Binh T. Nguyen, Nguyen Viet Cuong. Semi-supervised Learning for Vietnamese Named Entity Recognition using Online Conditional Random Fields. Named Entities Workshop @ ACL 2015.
  21. Nguyen Viet Cuong. Near-optimality and Robustness of Greedy Algorithms for Bayesian Pool-based Active Learning. PhD Thesis. Department of Computer Science, National University of Singapore, 2015.
  22. Nguyen Viet Cuong, Wee Sun Lee, Nan Ye. Near-optimal Adaptive Pool-based Active Learning with General Loss. Conference on Uncertainty in Artificial Intelligence (UAI 2014). Google Best Student Paper Award. (supplementary) (slides)
  23. Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu. Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation. Journal of Machine Learning Research (JMLR 2014). (code) (data)
  24. Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu. Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion. Advances in Neural Information Processing Systems (NIPS 2013).
  25. Nguyen Viet Cuong, Lam Si Tung Ho, Vu Dinh. Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data. International Conference on Algorithmic Learning Theory (ALT 2013).
  26. Nguyen Viet Cuong, Vu Dinh, Lam Si Tung Ho. Mel-frequency Cepstral Coefficients for Eye Movement Identification. IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2012). (code)