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.

7.      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, 2024. [Link]

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

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

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.    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.” Conditionally accepted at Neurocomputing, 2023.

12.    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.

13.    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)

14.    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.

15.    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.

16.    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.

17.    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.

18.    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]

19.    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]

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

21.    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.

22.    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.

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

24.    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]

25.    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]

26.    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.

27.    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.

28.    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.

29.    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.

30.    Anh Phan Tuan, Bach Tuan, Thien Nguyen Huu, Linh Ngo Van, Khoat Than, “Bag of biterms modeling for short texts”, Knowledge and Information Systems, 62, pages 4055-4090. Springer, 2020.

31.    Xuan Bui, Hieu Vu, Oanh Nguyen, Khoat Than, “MAP estimation with Bernoulli randomness, and its application to text analysis and recommender systems,” IEEE Access, 2020.

32.    Linh Ngo, Anh Nguyen-Duc, Binh Nguyen, Khoat Than. “Neural Poisson Factorization”. IEEE Access, 2020.

33.    Tuc Nguyen, Linh Ngo Van, Khoat Than, “Modeling the sequential behaviors of online users in recommender systems”, In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications. SPIE, 2020.

34.    Van-Son Nguyen, Duc-Tung Nguyen, Linh Ngo Van, Khoat Than, Infinite Dropout for training Bayesian models from data streams, Proceedings of IEEE International Conference on Big Data, Los Angeles, CA, USA, 2019. [PDF]

35.    Cuong Ha, Van-Dang Tran, Linh Ngo Van, Khoat Than, Eliminating overfitting of probabilistic topic models on short and noisy text: The role of Dropout, International Journal of Approximate Reasoning, Volume 112, Pages 85-104. Elsevier, 2019. [PDF] [Link]

36.    Linh The Nguyen, Linh Van Ngo, Khoat Than, and Thien Huu Nguyen, Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings, In Proceeding of the Association for Computational Linguistics. ACL, 2019. [Link]

37.    Anh Phan Tuan, Nhat Nguyen Trong, Duong Bui, Linh Van Ngo, Khoat Than, From Implicit to Explicit Feedbacks: A deep neural network for modeling the sequential behaviors of online users, In ACML. Proceeding of Machine Learning Research, 2019. [Link]

38.    Phuong Minh Nguyen, Khoat Than, Nguyen Le Minh, Marking Mechanism in Sequence-to-sequence Model for Mapping Language to Logical Form, In Proceedings of KSE. IEEE, 2019.

39.    Khoat Than, Xuan Bui, Tung Nguyen-Trong, Khang Truong, Son Nguyen, Bach Tran, Linh Ngo Van, and Anh Nguyen-Duc, How to make a machine learn continuously: a tutorial of the Bayesian approach, In Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, doi 10.1117/12.2518860. SPIE, 2019. [PDF] [Link]

40.    Thanh Hai Hoang, Anh Phan Tuan, Linh Ngo Van, Khoat Than, Enriching user representation in Neural Matrix Factorization, In Proceedings of the 2019 IEEE-RIVF International Conference on Computing and Communication Technologies. IEEE, 2019. [Link]

41.    Huy Do, Khoat Than, Pierre Larmande, Evaluating Named-Entity Recognition Approaches in Plant Molecular Biology, In Proceedings of MIWAI. Lecture Notes in Artificial Intelligence, Springer, vol. 11248, pp. 219-225, 2018.

42.    Nguyen Trong Tung, Vu Hoang Dieu, Khoat Than, Ngo Van Linh, Reducing Class Overlapping in Supervised Dimension Reduction, In Proceedings of the Ninth International Symposium on Information and Communication Technology (SoICT). ACM, 2018.

43.    Hoa Le Minh, Son Ta Cong, Quyen Pham The, Linh Ngo Van, Khoat Than, Collaborative Topic Model for Poisson distributed ratings, International Journal of Approximate Reasoning, Volume 95, Pages 62-76, Elsevier, 2018. [Link]

44.    Xuan Bui, Tu Vu, Khoat Than, Some Methods for Posterior Inference in Topic Models, Research and Development on Information and Communication Technology Journal (RD-ICT), Vol. E-3. No. 15, 2018.

45.    Bui Thanh-Xuan, Vu Van-Tu, Atsuhiro Takasu, Khoat Than, A fast algorithm for posterior inference with Latent Dirichlet Allocation, In ACIIDS, Lecture Notes in Computer Science, Springer, 2018.

46.    Tu Vu, Xuan Bui, Khoat Than, Ryutaro Ichise, A flexible stochastic method for solving the MAP problem in topic models, Computacion y Sistemas journal, 2018.

47.    Huy Do, Hanh Tran, Khoat Than, Pierre Larmande, Comparative study of Named-Entity Recognition methods in the agronomical domain, Computacion y Sistemas journal, 2018.

48.    Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Chien Nguyen Dang, An Effective and Interpretable Method for Document Classification, Knowledge and Information Systems, Volume 50, Issue 3, pp 763-793, 2017. [PDF] [Link]

49.    Tung Doan, Khoat Than, Sparse Stochastic Inference with Regularization, In Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Lecture Notes in Computer Science, Springer, 2017. [PDF]

50.    Duc-Anh Nguyen, Kim Anh Nguyen, Linh Ngo, Khoat Than, Keeping priors in streaming Bayesian learning, In Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Lecture Notes in Computer Science, Springer, 2017. [PDF]

51.    Xuan Bui, Tu Vu, Khoat Than, Stochastic bounds for inference in topic models, In Proceeding of ICTA, Springer. Volume 538 of the series Advances in Intelligent Systems and Computing, pages 582-592, 2016. [Link]

52.    Vu Le, Chien Phung, Cuong Vu, Ngo Van Linh, Khoat Than, Streaming Sentiment-Aspect Analysis, In Proceeding of IEEE RIVF,, pages 181-186, 2016. [Link]

53.    Khoat Than, Tung Doan, Guaranteed inference in topic models, Preprint, 2015. [Code] [Link]

54.    Khoat Than, Tu Bao Ho, Inference in topic models: sparsity and trade-off, Preprint, 2015. [Code] [Link]

55.    Mai Tien Khai, Mai Anh Sang, Nguyen Kim Anh, Ngo Van Linh, Khoat Than, Enabling Hierarchical Dirichlet Processes to work better for short texts at large scale, In Proceedings of PAKDD. Lecture Notes in Computer Science, Springer, 2016. [PDF]

56.    Ngo Van Linh, Nguyen Kim Anh, Khoat Than, Nguyen Tat Nguyen, Effective and Interpretable Document Classification using Distinctly Labeled Dirichlet Process Mixture Models of von Mises-Fisher Distributions, In Proceedings of DASFAA. Lecture Notes in Computer Science, Springer, 2015.

57.    Khoat Than and Tung Doan, Dual online inference for latent Dirichlet allocation. In ACML. Journal of Machine Learning Research: W&CP, 2014. [PDF]

58.    Khoat Than and Tu Bao Ho, Modeling the diversity and log-normality in data. Intelligent Data Analysis: An International Journal, IOS Press, vol. 18(6), pages 1067-1088, 2014. [Link]

59.    Khoat Than, Tu Bao Ho, and Duy Khuong Nguyen, An effective framework for supervised dimension reduction. Neurocomputing, Springer, vol. 139, pages 397-407, 2014. [PDF] [Code] [Link]

60.    Ngo Van Linh, Nguyen Kim Anh, Khoat Than, An effective NMF-based method for supervised dimension reduction, In Proceedings of KSE 2014. Advances in Intelligent Systems and Computing Volume 326, pages 93-104, 2015. Springer. [Link]

61.    Khoat Than and Tu Bao Ho, Probable convexity and its application to Correlated Topic Models. Technical report, 12/2013. [PDF] [Code]

62.    Duy Khuong Nguyen, Khoat Than, and Tu Bao Ho, Simplicial Nonnegative Matrix Factorization, In Proceedings of The 10th IEEE RIVF International Conference on Computing and Communication Technologies, 2013. (Best Student Paper Award) [Link]

63.    Khoat Than, Tu Bao Ho, Duy Khuong Nguyen, and Ngoc Khanh Pham. Supervised dimension reduction with topic models. In ACML. Journal of Machine Learning Research: W&CP, vol. 25, pages 395-410, 2012. [PDF] [Code] [Link]

64.    Khoat Than and Tu Bao Ho. Managing sparsity, time, and quality of inference in topic models. Technical report, 2012. [PDF] [Code] [Link]

65.    Khoat Than and Tu Bao Ho. Fully sparse topic models. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bristol, UK. Vol. 7523 of Lecture Notes in Computer Science, Springer, pages 490-505, 2012. [PDF] [Code] [Link]

66.    Than Quang Khoat. Relation between the Hardness of a Problem and the Number of its Solutions. Acta Mathematica Vietnamica, vol. 36(1), pages 55-60, 2011. [PDF] [Link]

67.    Than Quang Khoat and Nguyen Hong Tan, Unique Shortest Vector Problem for max norm is NP-hard. Vietnam Journal of Science and Technology, vol. 46(5A), pages 86-100.
(Special issue of The Second International Conference on Theories and Applications of Computer Science, ICTACS, February 2009, Nha Trang city, Vietnam.) [PDF]

68.    Than Quang Khoat. Xấp xỉ bài toán RSIVP và MISP với bội số giả đa thức là NP-hard. Vietnam Journal on Information Technologies and Communications, vol. 22, 2009. [PDF]

69.    Than Quang Khoat, Trần Thị Ngân, Bùi Thị Thanh Xuân, Nguyễn Văn Núi. Về một cách tiếp cận mới giải bài toán Quy hoạch tuyến tính nguyên. Thai Nguyen Journal of Science and Technology, vol. 56(8), pages 51-59, 2009.

70.    Than Quang Khoat, On the Bounded Integer Programming. In Proceedings of the 2008 IEEE International Conference on Research, Innovation & Vision for the Future - RIVF08, Ho Chi Minh city, Vietnam, pages 23-28, 2008. [PDF] [Link] (Student Best Paper Award)

71.    Than Quang Khoat, Nguyễn Hồng Tân and Nguyễn Văn Núi. Chứng minh độ khó NP-hard của một số bài toán lưới. Vietnam Journal on Information Technologies and Communications, No. 19(2), pages 124-133, 2008.