Publications

(See Google Scholar for a more detailed list.)

2025

Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Duc Nguyen, Toan Tran, David Leo Wright Hall, Cheongwoong Kang, Jaesik Choi. Neural ODE transformers: Analyzing internal dynamics and adaptive fine-tuning. ICLR, 2025.
Nguyen Hung-Quang, Ngoc-Hieu Nguyen, The-Anh Ta, Thanh Nguyen-Tang, Kok-Seng Wong, Hoang Thanh-Tung, and Khoa D Doan. Wicked oddities: Selectively poisoning for effective clean-label backdoor attacks. ICLR, 2025 [pdf].
Ragja Palakkadavath, Hung Le, Thanh Nguyen-Tang, Svetha Venkatesh, Sunil Gupta. Fair domain generalization with heterogeneous sensitive attributes across domains. WACV, 2025 [pdf].

Thanh Nguyen-Tang, Raman Arora. Learning in Markov games with adaptive adversaries: Policy regret, fundamental barriers, and efficient algorithms. NeurIPS, 2024 [pdf].
Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora. Adversarially robust multi-task representation learning. NeurIPS, 2024 [pdf].
Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup. Offline multitask representation learning for reinforcement learning. NeurIPS, 2024 [pdf].
Thanh Nguyen-Tang, Raman Arora. On the statistical complexity of offline decision-making. ICML, 2024 [pdf].

Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran, Jaesik Choi. SigFormer: Signature transformers for deep hedging. ICAIF, 2023 (Oral)[pdf].
Anh Do, Thanh Nguyen-Tang, Raman Arora. Multi-agent learning with heterogeneous linear contextual bandits. NeurIPS, 2023 [pdf].
Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, Raman Arora. Optimistic rates for multi-task representation learning. NeurIPS, 2023 [pdf]
Thanh Nguyen-Tang, Raman Arora. On sample-efficient offline reinforcement learning: Data diversity, posterior sampling and beyond. NeurIPS, 2023 [pdf].
Ragja Palakkadavath, Thanh Nguyen-Tang, Hung Le, Svetha Venkatesh, Sunil Gupta. Domain generalization with interpolation robustness. ACML, 2023 [pdf].
Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, Thanh Nguyen-Tang. Global contrastive learning for long-tailed classification. TMLR, 2023 [pdf].
A. Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, Philip Torr. TIPI: Test time adaptation with transformation invariance. CVPR, 2023 [html].
Thanh Nguyen-Tang, Raman Arora. VIPeR: Provably efficient algorithm for offline RL with neural function approximation. ICLR, 2023 (top 25% noble). [talk] [slides] [code] [ERRATUM.]
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora. On instance-dependent bounds for offline reinforcement learning with linear function approximation. AAAI, 2023 [arXiv] [poster] [slides] [video].

Anh Tong, Thanh Nguyen-Tang, Toan Tran, Jaesik Choi. Learning fractional white noises in neural stochastic differential equations. NeurIPS, 2022 [pdf] [code].
Thanh Nguyen-Tang, Sunil Gupta, A.Tuan Nguyen, and Svetha Venkatesh. Offline neural contextual bandits: Pessimism, optimization, and generalization. ICLR, 2022 [pdf] [poster] [slides] [code].
Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh. On sample complexity of offline reinforcement learning with deep ReLU networks in Besov spaces. TMLR, 2022, Workshop on RL Theory, ICML, 2021 [arXiv] [slides] [talk].

Thanh Nguyen-Tang, Sunil Gupta, Svetha Venkatesh. Distributional reinforcement learning via moment matching. AAAI, 2021 [arXiv] [code] [slides] [poster] [talk].

Thanh Nguyen-Tang, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh. Distributionally robust Bayesian quadrature optimization. AISTATS, 2020 [arXiv] [code] [slides] [talk].

Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen-Tang, Hung Tran-The, Svetha Venkatesh. Bayesian optimization with unknown search space. NeurIPS, 2019 [pdf] [code] [poster].
Thanh Nguyen-Tang, Jaesik Choi. Markov information bottleneck to improve information flow in stochastic neural networks. Entropy, 2019 (Invited, special Issue on Information Bottleneck: Theory and Applications in Deep Learning) [pdf].