Publications

Preprints

Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le, Canh T. Dinh, and Nguyen H. Tran. “On the Generalization of Wasserstein Robust Federated Learning.” arXiv:2206.01432 (2022). [Paper] [Code]

2024

Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Nguyen, and Kim-Chuan Toh. “On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods”. In: Proceedings of the AAAI Conference on Artificial Intelligence (to appear) 38 (2024).[Paper] [Code]

Tuan Dung Nguyen, Ziyu Chen, Nicholas George Carroll, Alasdair Tran, Colin Klein, and Lexing Xie. “Measuring Moral Dimensions in Social Media with Mformer”. In: Proceedings of the International AAAI Conference on Web and Social Media (to appear) (2024). [Paper] [Code and data]

2023

Tuan Dung Nguyen, Yuan-Sen Ting, Ioana Ciuca, Charles O’Neill, Ze-Chang Sun, Maja Jablonska, Sandor Kruk, Ernest Perkowski, Jack Miller, Jason Jingshi Li, Josh Peek, Kartheik Iyer, Tomasz Rozanski, Pranav Khetarpal, Sharaf Zaman, David Brodrick, Sergio J. Rodriguez Mendez, Thang Bui, Alyssa Goodman, Alberto Accomazzi, Jill Naiman, Jesse Cranney, Kevin Schawinski, and Roberta Raileanu. “AstroLLaMA: Towards Specialized Foundation Models in Astronomy”. In: Proceedings of the 2nd Workshop on Information Extraction from Scientific Publications. Bali, Indonesia: Association for Computational Linguistics, 2023, pp. 49–55. [Paper] [Model] [Presentation]

2022

Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Amir Rezaei Balef, Bing B. Zhou, and Albert Y. Zomaya. “DONE: Distributed Approximate Newton-type Method for Federated Edge Learning”. In: IEEE Transactions on Parallel and Distributed Systems 33.11 (2022), pp. 2648–2660. [Paper] [Code]

Tuan Dung Nguyen, Georgiana Lyall, Alasdair Tran, Minjeong Shin, Nicholas George Carroll, Colin Klein, and Lexing Xie. “Mapping Topics in 100,000 Real-Life Moral Dilemmas”. In: Proceedings of the International AAAI Conference on Web and Social Media 16.1 (2022), pp. 699–710. [Paper] [Code] [Data] [Poster] [Presentation] [Slides]

2021

Tuan Dung Nguyen, Amir R. Balef, Canh T. Dinh, Nguyen H. Tran, Duy T. Ngo, Tuan Anh Le, and Phuong L. Vo. “Accelerating Federated Edge Learning”. In: IEEE Communications Letters 25.10 (2021), pp. 3282–3286. [Paper] [Code for AA]

2020

Canh T. Dinh, Nguyen H. Tran, and Tuan Dung Nguyen. “Personalized Federated Learning with Moreau Envelopes”. In: Advances in Neural Information Processing Systems 33. 2020, pp. 21394–21405. [Paper] [Code] [Presentation]

Canh T. Dinh, Nguyen H. Tran, Tuan Dung Nguyen, Wei Bao, Albert Y. Zomaya, and Bing B. Zhou. “Federated Learning with Proximal Stochastic Variance Reduced Gradient Algorithms”. In: 49th International Conference on Parallel Processing. 2020, pp. 1–11. [Paper] [Code] [Presentation]