Comparer des méthodes

	Processus Gaussien Auto-supervisé	Processus Gaussien Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019–2021	1978–2006
Auteur d'origine	Fortuin, V. et al.; broader self-supervised GP literature	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic model (self-supervised GP pretraining + kernel learning)	Probabilistic kernel model
Source fondatrice	Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	SSL-GP, self-supervised GP, self-supervised GPR, self-supervised Gaussian process regression	GP regression, GPR, Gaussian process model, GP classifier
Apparentées	6	3
Résumé	Self-supervised Gaussian Process (SSL-GP) combines the principled uncertainty quantification of Gaussian processes with self-supervised pretraining, learning expressive kernels or latent representations from unlabeled data before fitting a GP on a small labeled set. This makes the approach especially powerful in low-labeled-data regimes where a conventional GP would overfit or produce poorly calibrated uncertainty estimates.	A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.