[49] SEEGER, M. Bayesian gaussian process models: Pac-bayesian generalisation error bounds and sparse approximations. Tech. rep., University of Edinburgh, 2003.
[50] SEeGER, M. Pac-bayesian generalisation error bounds for gaussian process classification. J. Mach. Learn. Res. 3, null (mar 2003), 233-269.
[51] SEEGER, M. W., WILLIAMS, C. K. I., AND LAWRENCE, N. D. Fast forward selection to speed up sparse gaussian process regression. In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (03–06 Jan 2003), C. M. Bishop and B. J. Frey, Eds., vol. R4 of Proceedings of Machine Learning Research, PMLR, pp. 254–261. Reissued by PMLR on 01 April 2021.
[52] SNELSON, E., AND GHAHRAMANI, Z. Sparse gaussian processes using pseudo-inputs. In Advances in Neural Information Processing Systems (2005), Y. Weiss, B. Scholkopf, and J. Platt, Eds., vol. 18, MIT Press.
[53] SNIEKERS, S., AND VAN DER VAART, A. Adaptive Bayesian credible sets in regression with a Gaussian process prior. Electronic Journal of Statistics 9, 2 (2015), 2475 - 2527.
[54] STUART, A. M. Inverse problems: a bayesian perspective. Acta numerica 19 (2010), 451-559.
[55] SZABO, B., VAN DER VAART, A. W., AND VAN ZANTEN, H. Frequentist coverage of adaptive nonparametric bayesian credible sets. Ann. Statist. 43, 4 (2015), 1391-1428. (with discussion).
[56] TIKHONOV, A. N. Solution of incorrectly formulated problems and the regularization method. Soviet Math. 4 (1963), 1035-1038.
[57] TITSIAS, M. Variational learning of inducing variables in sparse gaussian processes. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA, 16–18 Apr 2009), D. van Dyk and M. Welling, Eds., vol. 5 of Proceedings of Machine Learning Research, PMLR, pp. 567–574.
[58] TITSIAS, M. K. Variational model selection for sparse gaussian process regression. Report, University of Manchester, UK (2009).
[59] TRAVIS, L., AND RAY, K. Pointwise uncertainty quantification for sparse variational gaussian process regression with a brownian motion prior. NeurIPS 2023 (2023).
[60] VAKILI, S., SCARLETT, J., SHIU, D.-S., AND BERNACCHIA, A. Improved convergence rates for sparse approximation methods in kernel-based learning. In International Conference on Machine Learning (2022), PMLR, pp. 21960-21983.
[61] VAN DER VAART, A., AND VAN ZANTEN, H. Information rates of nonparametric Gaussian process methods. J. Mach. Learn. Res. 12 (2011), 2095-2119.
[62] VAN DER VAART, A. W., AND VAN ZANTEN, J. H. Rates of contraction of posterior distributions based on Gaussian process priors. Ann. Statist. 36, 3 (2008), 1435–1463.
[63] WANG, Y., AND BLEI, D. M. Frequentist consistency of variational Bayes. J. Amer. Statist. Assoc. 114, 527 (2019), 1147-1161.
[64] YANG, Y., PATI, D., AND BHATTACHARYA, A.  $\alpha$ -variational inference with statistical guarantees. Ann. Statist. 48, 2 (2020), 886-905.
[65] YANG, Y., AND TOKDAR, S. T. Minimax-optimal nonparametric regression in high dimensions. The Annals of Statistics 43, 2 (2015), 652 - 674.
[66] ZHANG, F., AND GAO, C. Convergence rates of variational posterior distributions. The Annals of Statistics 48, 4 (2020), 2180 - 2207.