Thanh Nguyen-Tang

tnt
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] [blog]

*I'm on the 2024-2025 job market [research statement].

Background

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

An overarching goal of my research is to establish Algorithmic Foundations of Learning for modern AI systems (AFLAI Lab), with the vision of enabling next-generation AI with better scalability, explainability, and transferability. My approach emphasizes understanding learning through the lens of critical resources (e.g., data, and computation) and designing optimal algorithms that use these resources efficiently. My research agenda for the AFLAI Lab spans four main thrusts:

I am also currently open to mentor and supervise students to work on these areas.

Publications

2024

20. Thanh Nguyen-Tang, Raman Arora. Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms. To appear in NeurIPS’24.
19. Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora. Adversarially Robust Multi-task Representation Learning. To appear in NeurIPS’24.
18. Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup. Offline Multitask Representation Learning for Reinforcement Learning. To appear in NeurIPS’24.
17. Thanh Nguyen-Tang, Raman Arora. On The Statistical Complexity of Offline Decision-Making. International Conference on Machine Learning (ICML), 2024.

2023

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). [talk] [slides] [code] [ERRATUM]
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].

2022

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].
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].

2021

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].

2020

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].

2019

2. 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].
1. 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).

Preprints

Mentoring

Teaching

* 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.

Selected Awards

Professional Service

Area Chair/Senior Program Committee

Conference Reviewer/Program Committee

Coordinator

Invited Talks