Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. Advances in neural information processing systems, 31, 2018.
Alicia Curth, Alihan Hüyük, and Mihaela van der Schaar. Adaptively identifying patient populations with treatment benefit in clinical trials. In International Conference on Machine Learning, 2023.
Marc Deisenroth and Carl E Rasmussen. Pilco: A model-based and data-efficient approach to policy search. In Proceedings of the 28th International Conference on machine learning (ICML-11), pages 465-472. Citeseer, 2011.
J Du, J Futoma, and F Doshi-Velez. Model-based reinforcement learning for semi-Markov decision processes with neural ODEs. In Proceedings of the 34th Conference on Neural Information Processing Systems, 2020.
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
Scott Fujimoto and Shixiang Shane Gu. A minimalist approach to offline reinforcement learning. Advances in neural information processing systems, 34:20132-20145, 2021.
Scott Fujimoto, Herke Hoof, and David Meger. Addressing function approximation error in actor-critic methods. In International conference on machine learning, pages 1587-1596. PMLR, 2018.
Changran Geng, Harald Paganetti, and Clemens Grassberger. Prediction of treatment response for combined chemo-and radiation therapy for non-small cell lung cancer patients using a biomathematical model. *Scientific reports*, 7(1):1-12, 2017.
Daniel T Gillespie. Markov processes: an introduction for physical scientists. Elsevier, 1991.
Agathe Girard, Carl Edward Rasmussen, J Quinonero-Candela, R Murray-Smith, O Winther, and J Larsen. Multiple-step ahead prediction for non linear dynamic systems-a gaussian process treatment with propagation of the uncertainty. Advances in neural information processing systems, 15:529-536, 2002.
David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018.
Nicklas Hansen, Xiaolong Wang, and Hao Su. Temporal difference learning for model predictive control. arXiv preprint arXiv:2203.04955, 2022.
David Hoeller, Farbod Farshidian, and Marco Hutter. Deep value model predictive control. In Conference on Robot Learning, pages 990-1004. PMLR, 2020.
Samuel Holt, Zhaozhi Qian, and Mihaela van der Schaar. Deep generative symbolic regression. In The Eleventh International Conference on Learning Representations, 2022a.
Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, and Mihaela van der Schaar. Neural Laplace control for continuous-time delayed systems. In International Conference on Artificial Intelligence and Statistics, 2023.
Samuel I Holt, Zhaozhi Qian, and Mihaela van der Schaar. Neural laplace: Learning diverse classes of differential equations in the laplace domain. In International Conference on Machine Learning, pages 8811-8832. PMLR, 2022b.
Yunhan Huang and Quanyan Zhu. Infinite-horizon linear-quadratic-gaussian control with costly measurements. arXiv preprint arXiv:2012.14925, 2020.
Yunhan Huang, Veeraruna Kavitha, and Quanyan Zhu. Continuous-time markov decision processes with controlled observations. In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 32-39. IEEE, 2019.
Alihan Hüyük, Zhaozhi Qian, and Mihaela van der Schaar. When to make and break commitments? In International Conference on Learning Representations, 2023.