[19] Peter I Frazier. A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811, 2018.
[20] Fred N. Fritsch and Judy Butland. A method for constructing local monotone piecewise cubic interpolants. Siam Journal on Scientific and Statistical Computing, 5:300-304, 1984.
[21] Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, and SM Ali Eslami. Conditional neural processes. In International Conference on Machine Learning, pages 1704-1713. PMLR, 2018.
[22] Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo Jimenez Rezende, S. M. Ali Eslami, and Yee Whye Teh. Neural processes. ArXiv, abs/1807.01622, 2018.
[23] Roman Garnett. Bayesian optimization. Cambridge University Press, 2023.
[24] Zoubin Ghahramani. A tutorial on gaussian processes. Summer School on Machine Learning, 6:67-80, 1999.
[25] Jonathan Gordon, Wessel P Bruinsma, Andrew YK Foong, James Requeima, Yann Dubois, and Richard E Turner. Convolutional conditional neural processes. In International Conference on Learning Representations, 2019.
[26] Alan Hajek. Dutch book arguments. In The Handbook of Rational and Social Choice, 2009.
[27] Houlsby, Huszár, Ghahramani, and Lengyel. Bayesian active learning for classification and preference learning. arXiv, 2011.
[28] Harry Joe. Multivariate models and dependence concepts. Chapman & Hall/CRC, 1997.
[29] Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, and Yutaka Matsuo. Group equivariant conditional neural processes. In International Conference on Learning Representations, 2020.
[30] Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh. Attentive neural processes. In International Conference on Learning Representations, 2018.
[31] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
[32] Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, and Joni Dambre. Bruno: A deep recurrent model for exchangeable data. Advances in Neural Information Processing Systems, 31, 2018.
[33] Iryna Korshunova, Yarin Gal, Arthur Gretton, and Joni Dambre. Conditional bruno: A neural process for exchangeable labelled data. Neurocomputing, 416:305-309, 2020. ISSN 0925-2312. doi: https://doi.org/10.1016/j.neucom.2019.11.108. URL https://www.sciencedirect.com/science/article/pii/S0925231220304987.
[34] Carlo R. Laing and Gabriel J. Lord. Stochastic methods in neuroscience. 2009.
[35] John Langford and Tong Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. Advances in neural information processing systems, 20(1):96-1, 2007.
[36] Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning, pages 3744-3753. PMLR, 2019.
[37] Xuechen Li, Ricky Tian Qi Chen, Ting-Kam Leonard Wong, and David Duvenaud. Scalable gradients for stochastic differential equations. In Artificial Intelligence and Statistics, 2020.
[38] Juan Marones, Oliver Hamelijnck, Jeremias Knoblauch, and Theodoros Damoulas. Transforming gaussian processes with normalizing flows. In International Conference on Artificial Intelligence and Statistics, pages 1081-1089. PMLR, 2021.