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[8] Kihyuk Hong, Ambuj Tewari. Offline Constrained Reinforcement Learning with Arbitrary Data Distributions under Partial Coverage. Preprint 2025. [paper]
[7] Marc Brooks*, Gabriel Durham*, Kihyuk Hong*, Ambuj Tewari. Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions. In Advances in Neural Information Processing Systems 38, 2025. [paper]
[6] Kihyuk Hong, Ambuj Tewari. A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs. In Proceedings of the 42nd International Conference on Machine Learning, 2025*.* [paper]
[5] Kihyuk Hong, Woojin Chae, Yufan Zhang, Dabeen Lee, Ambuj Tewari. Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, 2025. [paper]
[4] Woojin Chae, Kihyuk Hong, Yufan Zhang, Ambuj Tewari, Dabeen Lee. Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, 2025. [paper]
[3] Kihyuk Hong, Ambuj Tewari. A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs. In Proceedings of the 41st International Conference on Machine Learning, 2024*.* [paper]
[2] Kihyuk Hong, Yuhang Li, Ambuj Tewari. A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 2024. [paper]
[1] Kihyuk Hong, Yuhang Li, Ambuj Tewari. An Optimization-Based Algorithm for Non-Stationary Kernel Bandits without Prior Knowledge. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, 2023. [paper]