[1] Michael Andreas Erdmann. On motion planning with uncertainty. 1984.
[2] Mykel J Kochenderfer, Tim A Wheeler, and Kyle H Wray. Algorithms for decision making. MIT press, 2022.
[3] Jonathan Ko and Dieter Fox. Gp-bayesfilters: Bayesian filtering using gaussian process prediction and observation models. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3471-3476, 2008.
[4] Christian Soize. Uncertainty quantification. Springer, 2017.
[5] Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski. Robust optimization, volume 28. Princeton university press, 2009.
[6] Chancellor Johnstone and Bruce Cox. Conformal uncertainty sets for robust optimization. In Conformal and Probabilistic Prediction and Applications, pages 72–90. PMLR, 2021.
[7] Rinu Boney, Juho Kannala, and Alexander Ilin. Regularizing model-based planning with energy-based models. In Conference on Robot Learning, pages 182-191. PMLR, 2020.
[8] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[9] Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning, 2022.
[10] Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137, 2023.
[11] Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou. Diffusion policies as an expressive policy class for offline reinforcement learning. In The Eleventh International Conference on Learning Representations, 2023.
[12] Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma. Mopo: Model-based offline policy optimization. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 14129-14142. Curran Associates, Inc., 2020.
[13] Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi. An optimistic perspective on offline reinforcement learning. In International Conference on Machine Learning, pages 104-114. PMLR, 2020.
[14] Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh. Uncertainty weighted actor-critic for offline reinforcement learning. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 11319-11328. PMLR, 18-24 Jul 2021.
[15] Tilmann Gneiting and Adrian E Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 102(477):359-378, 2007.
[16] Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery. Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2):243-268, 2007.
[17] Shengjia Zhao, Michael Kim, Roshni Sahoo, Tengyu Ma, and Stefano Ermon. Calibrating predictions to decisions: A novel approach to multi-class calibration. Advances in Neural Information Processing Systems, 34:22313-22324, 2021.
[18] Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic. Revisiting the calibration of modern neural networks. Advances in Neural Information Processing Systems, 34:15682-15694, 2021.