Thanh Nguyen-Tang

Postdoctoral Research Fellow

Department of Computer Science
Whiting School of Engineering
Johns Hopkins University
Malone Hall 345, 3400 N Charles Street, Baltimore, MD 21218
nguyent at cs dot jhu dot edu / thnguyentang at gmail dot com
[Google Scholar]


I am currently a postdoc at Johns Hopkins University (with Raman Arora). Prior to that, I did my PhD in Computer Science at The Applied AI Institute, Deakin University, Australia (Alfred Deakin Medal for Doctoral Theses). I did my M.Sc. in Computer Science at Ulsan National Institute of Science and Technology, South Korea. In my previous life, I studied Electronic and Communication Engineering (Talented Engineering Program) at Danang University of Science and Technology, Vietnam.

Research Interest

 — Make the world an \(\epsilon\)-better place

My primary research interests lie in the statistical and computational aspects of machine learning, and its intersection with data-driven sequential decision-making, game theory, and optimization. More specifically, I am currently interested in the following problems: offline decision-making, learning in strategic environments, and learning with privacy constraints.

I have written papers about: offline decision-making in large state spaces (e.g., [ICML24] [NeurIPS23] [ICLR23] [AAAI23] [ICLR22] [TMLR22]), (multi-task) representation learning (e.g., [NeurIPS23]), MARL (e.g., [NeurIPS23]), distributional reinforcement learning (e.g., [AAAI21]), robust sequential decision-making (e.g., [AISTATS20]).

Selected Awards


* I participated in (and obtained a certificate of) Justice, Equity, Diversity, and Inclusion (JEDI) Training in the Classroom in March 2024 at JHU, as an effort to improve diversity in my future classes and research group.


All Publications


17. Thanh Nguyen-Tang, Raman Arora. On The Statistical Complexity of Offline Decision-Making. International Conference on Machine Learning (ICML), 2024.


16. Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran, Jaesik Choi. SigFormer: Signature Transformers for Deep Hedging. 4th ACM International Conference on AI in Finance (ICAIF), 2023 (Oral).
15. Anh Do, Thanh Nguyen-Tang, Raman Arora. Multi-Agent Learning with Heterogeneous Linear Contextual Bandits. Advances in Neural Information Processing Systems (NeurIPS), 2023.
14. Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora. Optimistic Rates for Multi-Task Representation Learning. Advances in Neural Information Processing Systems (NeurIPS), 2023.
13. Thanh Nguyen-Tang, Raman Arora. On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond. Advances in Neural Information Processing Systems (NeurIPS), 2023.
12. Ragja Palakkadavath, Thanh Nguyen-Tang, Hung Le, Svetha Venkatesh, Sunil Gupta. Domain Generalization with Interpolation Robustness. Asian Conference on Machine Learning (ACML), 2023.
11. Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, Thanh Nguyen-Tang. Global Contrastive Learning for Long-Tailed Classification. Transactions on Machine Learning Research (TMLR), 2023.
10. A. Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, Philip Torr. TIPI: Test Time Adaptation with Transformation Invariance. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
9. Thanh Nguyen-Tang, Raman Arora. VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation International Conference on Learning Representations (ICLR), 2023 (top 25% noble) [ERRATUM]. [talk] [slides] [code].
8. Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora. On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation. AAAI Conference on Artificial Intelligence (AAAI), 2023 [arXiv] [poster] [slides] [video].


7. Anh Tong, Thanh Nguyen-Tang, Toan Tran, Jaesik Choi. Learning Fractional White Noises in Neural Stochastic Differential Equations. Advances in Neural Information Processing Systems (NeurIPS), 2022. [code]. [arXiv].
6. Thanh Nguyen-Tang, Sunil Gupta, A.Tuan Nguyen, and Svetha Venkatesh. Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization. International Conference on Learning Representations (ICLR), 2022. [arXiv] [poster] [slides] [code].
5. Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh. On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces. Transactions on Machine Learning Research (TMLR), 2022, Workshop on RL Theory, ICML, 2021. [arXiv] [slides] [talk].


4. Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh. Distributional Reinforcement Learning via Moment Matching. AAAI Conference on Artificial Intelligence (AAAI), 2021. [arXiv] [code] [slides] [poster] [talk].


3. Thanh Nguyen-Tang, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh. Distributionally Robust Bayesian Quadrature Optimization. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [arXiv] [code] [slides] [talk].


2. Thanh Nguyen-Tang, Jaesik Choi. Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks. Entropy, 2019 (Special Issue on Information Bottleneck: Theory and Applications in Deep Learning).
1. Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen-Tang, Hung Tran-The, Svetha Venkatesh. Bayesian Optimization with Unknown Search Space. Advances in Neural Information Processing Systems (NeurIPS), 2019. [code] [poster].


Outreach & Service

Invited Talks

Professional Service