Anderes, E. (2010). On the consistent separation of scale and variance for Gaussian random fields. The Annals of Statistics.
Apanasovich, T. V. and M. G. Genton (2010). Cross-covariance functions for multivariate random fields based on latent dimensions. Biometrika 97(1), 15-30.
Banerjee, S., B. P. Carlin, and A. E. Gelfand (2014). Hierarchical modeling and analysis for spatial data. CRC press.
Cheng, L., S. Ramchandran, T. Vatanen, N. Lietzén, R. Lahesmaa, A. Vehtari, and H. Lähdesmäki (2019). An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data. Nature Communications 10(1), 1798.
Cressie, N. and C. K. Wikle (2015). Statistics for spatio-temporal data. John Wiley & Sons.
Duvenaud, D., J. Lloyd, R. Grosse, J. Tenenbaum, and G. Zoubin (2013, 17-19 Jun). Structure discovery in nonparametric regression through compositional kernel search. In S. Dasgupta and D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning, Volume 28 of Proceedings of Machine Learning Research, Atlanta, Georgia, USA, pp. 1166-1174. PMLR.
Duvenaud, D. K., H. Nickisch, and C. Rasmussen (2011). Additive Gaussian processes. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger (Eds.), Advances in Neural Information Processing Systems, Volume 24. Curran Associates, Inc.
Genton, M. G. (2001). Classes of kernels for machine learning: a statistics perspective. Journal of machine learning research 2(Dec), 299-312.
Genton, M. G. and W. Kleiber (2015). Cross-Covariance Functions for Multivariate Geostatistics. Statistical Science 30(2), 147 - 163.
Gneiting, T. (2002). Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association 97(458), 590-600.
Kingma, D. P. and J. Ba (2015). Adam: A method for stochastic optimization. In Y. Bengio and Y. LeCun (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.
Kronberger, G. and M. Kommenda (2013). Evolution of covariance functions for Gaussian process regression using genetic programming.
Lalchand, V., A. Ravuri, and N. D. Lawrence (2022). Generalised gplvm with stochastic variational inference. In International Conference on Artificial Intelligence and Statistics, pp. 7841-7864. PMLR.
Lawrence, N. (2003). Gaussian process latent variable models for visualisation of high dimensional data. In S. Thrun, L. Saul, and B. Schölkopf (Eds.), Advances in Neural Information Processing Systems, Volume 16. MIT Press.
LeCun, Y., C. Cortes, and C. J. Burges (1998). The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/. Accessed: 2023-08-01.
Li, D., W. Tang, and S. Banerjee (2023). Inference for gaussian processes with matérén covariogram on compact riemannian manifolds. Journal of Machine Learning Research 24(101), 1-26.
Lloyd, J. R., D. Duvenaud, R. Grosse, J. B. Tenenbaum, and Z. Ghahramani (2014). Automatic construction and natural-language description of nonparametric regression models.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
Rasmussen, C. E. and C. K. I. Williams (2006). Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press.