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Personal information

Fullname: Khoat Than (Thân Quang Khoát)

Affiliation: Associate Professor, School of Information & Communication Technology,

Hanoi University of Science and Technology

(Đại Học Bách Khoa Hà Nội)

Address: Room 706, Building B1, No. 1, Dai Co Viet road, Hanoi, Vietnam.

Email: khoattq @ soict dot hust dot edu dot vn

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Selected publications:

1.     Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Tran Khanh, Dung D. Le, Long Tran-Thanh, Khoat Than. "Provably Improving Generalization of Few-shot models with Synthetic Data". International Conference on Machine Learning (ICML), 2025.

2.     Khoat Than, Dat Phan, and Giang Vu. "Gentle Local Robustness implies Generalization." Machine Learning, 114(142), 2025. [Link][PDF]

3.     Lichuan Xiang, Quan Nguyen-Tri, Lan-Cuong Nguyen, Hoang Pham, Khoat Than, Long Tran-Thanh, Hongkai Wen. "DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle". In Proceedings of the Conference on Learning Representations, 2025. [Link]

4.     Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Toan Tran, Khoat Than. “On inference stability for diffusion models”. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. [Oral, top 2% of all submissions]

5.     Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui. “Structured Dropout Variational Inference for Bayesian Neural Networks”. In Advances in Neural Information Processing Systems (NeurIPS), 2021. [Link]

6.     Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui. “Predictive Coding for Locally-Linear Control”. In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.

 

Patent:

7.     Son Nguyen, Khai Nguyen, Khoat Than, Hung Bui, “Hệ thống và phương pháp suy diễn biến phân Dropout có cấu trúc”, Submission No. 1-2021-07770, IP Office of Vietnam.

 

Recent publications: (see Full list)

1.     Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Tran Khanh, Dung D. Le, Long Tran-Thanh, Khoat Than. "Provably Improving Generalization of Few-shot models with Synthetic Data". International Conference on Machine Learning (ICML), 2025.

2.     Khoat Than, Dat Phan, and Giang Vu. "Gentle Local Robustness implies Generalization." Machine Learning, 114(142), 2025. [Link][PDF]

3.     Lichuan Xiang, Quan Nguyen-Tri, Lan-Cuong Nguyen, Hoang Pham, Khoat Than, Long Tran-Thanh, Hongkai Wen. "DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle". In Proceedings of the Conference on Learning Representations, 2025. [Link]

4.     Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Khoat Than, Dinh Phung, Trung Le. "Boosting Multiple Views for pretrained-based Continual Learning". In Proceedings of the Conference on Learning Representations, 2025. [Link]

5.     Tung Nguyen, Tung Pham, Linh Ngo Van, Ha-Bang Ban, Khoat Than, “Out-of-vocabulary handling and topic quality control strategies in streaming topic models”, Neurocomputing, 614, 2025.

6.     Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Toan Tran, Khoat Than. “On inference stability for diffusion models”. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024. [Oral, Top 2% of all submissions]

7.     Tung Doan, Tuan Phan, Phu Nguyen, Khoat Than, Muriel Visani, Atsuhiro Takasu, “Partial Ordered Wasserstein Distance for Sequential Data”, Neurocomputing, 595, 2024.

8.     Tung Tran, Khoat Than, Danilo Vargas, “Robust visual reinforcement learning by prompt tuning”, ACCV, Springer, 2024.

9.     Nam Le Hai, Trang Nguyen, Linh Ngo Van, Thien Huu Nguyen, Khoat Than. “Continual variational dropout: a view of auxiliary local variables in continual learning”. Machine Learning, Volume 113, pages 281-323, 2024. [Link]

10.  Bach Tran, Anh Nguyen-Duc, Linh Ngo, Khoat Than. “Dynamic transformation of prior knowledge into Bayesian models for data streams”. IEEE Transactions on Knowledge and Data Engineering, 2023. [Link]

11.  Khang Nguyen, Kien Duc Do, Truong Tuan Vu, Khoat Than. “Unsupervised Image Segmentation with Robust Virtual Class Contrast.” Pattern Recognition Letters, Volume 173, Pages 10-16, 2023.

12.  Lam Tran, Viet Nguyen, Phi Nguyen, Khoat Than. “Sharpness and Gradient Aware Minimization for Memory-based Continual Learning”. International Symposium on Information and Communication Technology (SOICT), 2023. (Best Paper Runner-Up Award)

13.  Duy-Tung Nguyen, Duc-Manh Nguyen, Dinh-Tan Pham, Khoat Than, Hong-Thai Pham, Hai Vu. “Bayesian method for bee counting with noise-labeled data”. International Symposium on Information and Communication Technology (SOICT), 2023.

14.  Ha Nguyen, Hoang Pham, Son Nguyen, Linh Ngo Van, Khoat Than. “Adaptive Infinite Dropout for Noisy and Sparse Data Streams,” Machine Learning, 111, pages 3025-3060, 2022.

15.  Tung Nguyen, Trung Mai, Nam Nguyen, Linh Ngo Van, Khoat Than. “Balancing stability and plasticity when learning topic models from short and noisy text streams”. Neurocomputing, Volume 505, Pages 30-43, 2022.

16.  Son-Tung Tran, Van-Hung Le, Van-Nam Hoang, Khoat Than, Thanh-Hai Tran, Hai Vu, Thi-Lan Le. “A Local Structure-aware 3D Hand Pose Estimation Method for Egocentric Videos”. IEEE Ninth International Conference on Communications and Electronics (ICCE), 2022.

17.  Quyen Tran, Lam Tran, Linh Chu Hai, Linh Ngo, Khoat Than. “From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviors and long-short term preferences of online users”. Neurocomputing, Volume 479, Pages 89-105, Elsevier, 2022. [Link]

18.  Linh Ngo Van, Bach Tran, Khoat Than. “A graph convolutional topic model for short and noisy text streams”. Neurocomputing, Volume 468, Pages 345-359, Elsevier, 2022. [Link]

19.  Dieu Vu, Khang Truong, Khanh Nguyen, Linh Ngo, Khoat Than, “Revisiting Supervised Word Embeddings”, Journal of Information Science and Engineering, 2022.

20.  Linh Ngo Van, Nam Le Hai, Hoang Pham, Khoat Than. “Auxiliary Local Variables for Improving Regularization/prior Approach in Continual Learning”. Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science. Springer, 2022.

21.  Hoang Phan, Anh Phan, Son Nguyen, Linh Ngo Van, Khoat Than. “Reducing catastrophic forgetting in neural networks via Gaussian mixture approximation”. Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science. Springer, 2022.

22.  Khoat Than, Nghia Vu. “Generalization of GANs and overparameterized models under Lipschitz continuity”. arXiv:2104.02388, 2021. [Link]

23.  Son Nguyen, Duong Nguyen, Khai Nguyen, Nhat Ho, Khoat Than, Hung Bui. “Structured Dropout Variational Inference for Bayesian Neural Networks”. In Advances in Neural Information Processing Systems (NeurIPS), 2021. [Link]

24.  Nguyen, V.T., Le, T.T.K., Than, K., Tran, D.H. “Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities.” Scientific Report, 11, 21071. Nature 2021. [Link]

25.  Tuc Nguyen, Doanh Mai, Simon Su, Khoat Than. “An investigation of Graph Convolutional Networks for Collaborative Filtering: The role of nonlinear user-item prediction”. 2022.

26.  Tien-Cuong Nguyen, Van-Quyen Nguyen, Van-Linh Ngo, Khoat Than, Tien-Lam Pham. "Learning Hidden Chemistry with Deep Neural Networks". Computational Materials Science, Volume 200, 110784, Elsevier, 2021.

27.  Duc Anh Nguyen, Van Linh Ngo, Nguyen Kim Anh, Canh Hao Nguyen, Khoat Than, “Boosting priors in streaming Bayesian learning”, Neurocomputing, Volume 424, Pages 143-159. Elsevier, 2021.

28.  Rui Shu, Tung Nguyen, Yinlam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui. “Predictive Coding for Locally-Linear Control”. In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.