| Partially collapsed Gibbs sampling for latent Dirichlet allocation H Park, T Park, YS Lee Expert Systems with Applications 131, 208-218, 2019 | 26 | 2019 |
| Analysis of Thompson sampling for partially observable contextual multi-armed bandits H Park, MKS Faradonbeh IEEE Control Systems Letters 6, 2150-2155, 2021 | 15 | 2021 |
| Worst-case performance of greedy policies in bandits with imperfect context observations H Park, MKS Faradonbeh 2022 IEEE 61st Conference on Decision and Control (CDC), 1374-1379, 2022 | 5 | 2022 |
| Efficient algorithms for learning to control bandits with unobserved contexts H Park, MKS Faradonbeh IFAC-PapersOnLine 55 (12), 383-388, 2022 | 5 | 2022 |
| Analysis of patterns in meteorological research and development using a text-mining algorithm H Park, H Kim, T Park, YS Lee The Korean Journal of Applied Statistics 29 (5), 935-947, 2016 | 3 | 2016 |
| A regret bound for greedy partially observed stochastic contextual bandits H Park, MKS Faradonbeh Decision Awareness in Reinforcement Learning Workshop at ICML 2022, 2022 | 1 | 2022 |
| Thompson Sampling in Partially Observable Contextual Bandits H Park, MKS Faradonbeh arXiv preprint arXiv:2402.10289, 2024 | | 2024 |
| Sequentially Adaptive Experimentation for Learning Optimal Options subject to Unobserved Contexts H Park, MKS Faradonbeh NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in …, 2023 | | 2023 |
| Online Learning of Optimal Prescriptions under Bandit Feedback with Unknown Contexts H Park, MKS Faradonbeh NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development, 2023 | | 2023 |
| Balancing exploration and exploitation in Partially Observed Linear Contextual Bandits via Thompson Sampling H Park, MKS Faradonbeh ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023 | | 2023 |
