Comparer des méthodes

	Processus Gaussien Auto-supervisé	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019–2021	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Fortuin, V. et al.; broader self-supervised GP literature	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic model (self-supervised GP pretraining + kernel learning)	Probabilistic non-parametric 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, Gaussian Process Regression, GPR, Kriging
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 Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.