1. Adams, R.P., Murray, I., MacKay, D.J. (2009). Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. In: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, pp 9–16.
2. Aglietti, V., Damoulas, T., Bonilla, E.V. (2019). Efficient inference in multi-task Cox process models. In: The 22nd International Conference on Artificial Intelligence and Statistics, PMLR, pp 537–546.
3. Alvarez, M.A., Lawrence, N.D. (2008). Sparse convolved Gaussian processes for multi-output regression. In: NIPS, pp 57–64.
4. Álvarez, M. A., Rosasco, L., & Lawrence, N. D. (2012). Kernels for vector-valued functions: A review. Foundations and Trends in Machine Learning, 4(3), 195–266.
5. Álvarez, M.A., Ward, W., Guarnizo, C. (2019). Non-linear process convolutions for multi-output Gaussian processes. In: The 22nd International Conference on Artificial Intelligence and Statistics, PMLR, pp 1969–1977.