So sánh phương pháp

	Hồi quy Gaussian tự giám sát	Gaussian Process Bán Giám Sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2019–2021	2004
Người khởi xướng	Fortuin, V. et al.; broader self-supervised GP literature	Lawrence, N. D. & Jordan, M. I.
Loại	Probabilistic model (self-supervised GP pretraining + kernel learning)	Probabilistic model (semi-supervised)
Công trình gốc	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.	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.
Tên gọi khác	SSL-GP, self-supervised GP, self-supervised GPR, self-supervised Gaussian process regression	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning
Liên quan	6	5
Tóm tắt	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.	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.