Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in a variety of settings, including GP classification, nonparametric count regression, modeling non-Gaussian spatial data, ...
Spatially resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of local interactions between cells. However, it remains difficult to precisely align spatial observations acros
high-dimensional datasets render it invaluable in this domain [2], [3]. Moreover, the GPM offers robustness to outliers and noise, while also providing uncertainty quantification, thereby facilitating data-driven decision-making and enhancing predictions in diverse chemical processes and applications [...
structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel def...
Carl Edward Rasmussen, and Hannes Nickisch, Gaussian Processes for Machine Learning (GPML) Toolbox, J. Mach. Learn. Res., 11:3011–3015, 2010. pdf James Hensman, Nicolò Fusi, and Neil D. Lawrence, Gaussian Processes for Big Data, in Proceedings of Conference on Uncertainty in Artificial ...
Lawrence. "Gaussian processes for big data."arXiv preprint arXiv:1309.6835(2013). [4] Bradshaw, John, Alexander G. de G. Matthews, and Zoubin Ghahramani. "Adversarial examples, uncertainty, and transfer testing robustness in gaussian process hybrid deep networks."arXiv preprint arXiv:1707.02476(...
Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a ...
Gaussian Process regression is a kernel method successfully adopted in many real-life applications. Recently, there is a growing interest on extending this
Fusi, N.D. Lawrence, Gaussian processes for big data, arXiv e-prints, page arXiv:1309.6835, September 2013. Google Scholar [33] E. Snelson, Z. Ghahramani, Sparse gaussian processes using pseudo-inputs, in: Proceedings of the 18th International Conference on Neural Information Processing ...
Gaussian processes for big data. In: Proc. UAI. Washington, U.S.A., pp. 282–290. Google Scholar Hensman et al., 2015 Hensman, J., Matthews, A., Ghahramani, Z., 2015. Scalable variational Gaussian process classification. In: Proc. AISTATS. San Diego, U.S.A., pp. 1648–1656. ...