2026
Thanh Nguyen-Tang and Raman Arora. Exact unlearning in reinforcement learning. International Conference on Machine Learning (ICML), 2026. (Spotlight, top 2.2%)
Thong Bach, Thanh Nguyen-Tang, Dung Nguyen, Thao Minh Le, and Truyen Tran. Curvature-aware safety restoration in LLMs fine-tuning. Transactions on Machine Learning Research (TMLR), 2026.
2025
Thanh Nguyen-Tang and Raman Arora. Policy regret minimization in Markov games with function approximation. International Conference on Machine Learning (ICML), 2025.
Yassine Chemingui, Aryan Deshwal, Alan Fern, Thanh Nguyen-Tang, and Jana Doppa. Online optimization for offline safe reinforcement learning. Advances in Neural Information Processing Systems (NeurIPS), 2025.
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Duc Nguyen, Toan Tran, David Leo Wright Hall, Cheongwoong Kang, and Jaesik Choi. Neural ODE transformers: Analyzing internal dynamics and adaptive fine-tuning. International Conference on Learning Representations (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. International Conference on Learning Representations (ICLR), 2025.
Ragja Palakkadavath, Hung Le, Thanh Nguyen-Tang, Svetha Venkatesh, and Sunil Gupta. Federated domain generalization with latent space inversion. IEEE International Conference on Data Mining (ICDM), 2025.
Khai Le-Duc, Tuyen Tran, Nguyen Kim Hai Bui Bach Phan Tat, Quan Dang, Thanh-Thuy Nguyen, Hung-Phong Tran, Ly Nguyen, Tuan-Minh Phan, Thi Thu Phuong Tran, Chris Ngo, Nguyen X Khanh, and Thanh Nguyen-Tang. Multimed-ST: Large-scale many-to-many multilingual medical speech translation. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2025.
Khai Le-Duc, Phuc Phan, Tan-Hanh Pham, Bach Phan Tat, Minh-Huong Ngo, Thanh Nguyen-Tang, and Truong-Son Hy. MultiMed: Multilingual medical speech recognition via attention encoder decoder. 63rd Annual Meeting of the Association for Computational Linguistics (ACL), 2025.
Ragja Palakkadavath, Hung Le, Thanh Nguyen-Tang, Svetha Venkatesh, and Sunil Gupta. Fair domain generalization with heterogeneous sensitive attributes across domains. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025.
2024
Thanh Nguyen-Tang and Raman Arora. On the statistical complexity of offline decision-making. International Conference on Machine Learning (ICML), 2024.
Thanh Nguyen-Tang and Raman Arora. Learning in Markov games with adaptive adversaries: Policy regret, fundamental barriers, and efficient algorithms. Advances in Neural Information Processing Systems (NeurIPS), 2024.
Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, and Raman Arora. Adversarially robust multitask representation learning. Advances in Neural Information Processing Systems (NeurIPS), 2024.
Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, and Doina Precup. Offline multitask representation learning for reinforcement learning. Advances in Neural Information Processing Systems (NeurIPS), 2024.
Ragja Palakkadavath, Thanh Nguyen-Tang, Hung Le, Svetha Venkatesh, and Sunil Gupta. Domain generalization with interpolation robustness. Asian Conference on Machine Learning (ACML), 2024.
2023
Thanh Nguyen-Tang and Raman Arora. VIPeR: Provably efficient algorithm for offline RL with neural function approximation. International Conference on Learning Representations (ICLR), 2023. (top-15%-notable)
Thanh Nguyen-Tang and Raman Arora. On sample-efficient offline reinforcement learning: Data diversity, posterior sampling and beyond. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Anh Do, Thanh Nguyen-Tang, and Raman Arora. Multi-agent learning with heterogeneous linear contextual bandits. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Austin Watkins, Enayat Ullah, Thanh Nguyen-Tang, and Raman Arora. Optimistic rates for multitask representation learning. Advances in Neural Information Processing Systems (NeurIPS), 2023.
Anh Tong, Thanh Nguyen-Tang, Toan Tran, and Jaesik Choi. SigFormer: Signature transformers for deep hedging. Fourth ACM International Conference on AI in Finance (ICAIF), 2023. (Oral)
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, and Raman Arora. On instance-dependent bounds for offline reinforcement learning with linear function approximation. AAAI Conference on Artificial Intelligence (AAAI), 2023.
Thong Bach, Anh Tong, Truong Son Hy, Vu Nguyen, and Thanh Nguyen-Tang. Global contrastive learning for long-tailed classification. Transactions on Machine Learning Research (TMLR), 2023.
A Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, and Philip H.S. Torr. TIPI: Test time adaptation with transformation invariance. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
2022
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.
Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, and Svetha Venkatesh. On sample complexity of offline reinforcement learning with deep ReLU networks in Besov spaces. Transactions on Machine Learning Research (TMLR), 2022.
Anh Tong, Thanh Nguyen-Tang, Toan Tran, and Jaesik Choi. Learning fractional white noises in neural stochastic differential equations. Advances in Neural Information Processing Systems (NeurIPS), 2022.
2021
Thanh Nguyen-Tang, Sunil Gupta, and Svetha Venkatesh. Distributional reinforcement learning via moment matching. AAAI Conference on Artificial Intelligence (AAAI), 2021.
2020
Thanh Nguyen-Tang, Sunil Gupta, Huong Ha, Santu Rana, and Svetha Venkatesh. Distributionally robust Bayesian quadrature optimization. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
2019
Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen-Tang, and Svetha Venkatesh. Bayesian optimization with unknown search space. Advances in Neural Information Processing Systems (NeurIPS), 2019.
Thanh Nguyen-Tang and Jaesik Choi. Markov information bottleneck to improve information flow in stochastic neural networks. Entropy, 21(10):976, 2019.
