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.
I care about: science, teaching (education, broadly), and diversity. I love mathematics but would not consider myself a mathematician; I'm more as a ML scientist.
My research interest is statistical learning and decision-making (theory). I am currently working on: (offline) reinforcement learning, multitask learning, multi-agent learning.
— Make the world an \(\epsilon\)-better place
(see here for all publications or my Google Scholar)
[NeurIPS23a] On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling and Beyond, Thanh Nguyen-Tang, Raman Arora. Advances in Neural Information Processing Systems (NeurIPS), 2023.
[ICLR23] VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation, Thanh Nguyen-Tang, Raman Arora. International Conference on Learning Representations (ICLR), 2023 (noble-top-25%- Spotlight).
[AAAI23] On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation, Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora. AAAI Conference on Artificial Intelligence (AAAI), 2023.
[ICLR22] Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization, Thanh Nguyen-Tang, Sunil Gupta, A.Tuan Nguyen, and Svetha Venkatesh. International Conference on Learning Representations (ICLR), 2022.
[TMLR22] On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces, Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh. Transactions on Machine Learning Research (TMLR), 2022.
[AAAI21] Distributional Reinforcement Learning via Moment Matching, Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh. AAAI Conference on Artificial Intelligence (AAAI), 2021.
[AISTATS20] Distributionally Robust Bayesian Quadrature Optimization, Thanh Nguyen-Tang, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
[NeurIPS23c] Optimistic Rates for Multi-Task Representation Learning, Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora. Advances in Neural Information Processing Systems (NeurIPS), 2023.
[NeurIPS23b] Multi-Agent Learning with Heterogeneous Linear Contextual Bandits, Anh Do, Thanh Nguyen-Tang, Raman Arora. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Alfred Deakin Medal for Doctoral Theses (for the most outstanding theses), 2022.
— Knowledge is power
Co-instructor (with Raman Arora), Machine Learning: Advanced Topics: Foundations of Data-Driven Sequential Decision-Making Systems (CS 779), JHU, Spring 2024.
Teaching RL Theory in our JHU ML reading group, Summer/Fall 2023. [notes]
Guest lecturer (in bandits/reinforcement learning): Machine Learning (CS 475/675) Spring 2023, JHU. [notes]
Teaching Assistant: Statistical Machine Learning, Fall 2017, UNIST; Engineering Programming I/II, Spring 2016, UNIST; Various advanced mathematics and engineering courses, 2012-2016, Vietnam.
Ragja Palakkadavath (PhD student at Deakin University, out-of-distribution generalization)
Thong Bach (independent researcher, self-supervised learning and domain adaptation)
Anh Do (PhD student at JHU, bandit/reinforcement learning)
Austin Watkins (PhD student at JHU, transfer learning and robustness)
Cao Tinh (grad student at KU Leuven, learning theory in economic settings)