Methoden vergelijken

	Bayesiaans Actief Leren	Bayesian Optimization
Vakgebied	Machine learning	Optimalisatie
Familie	Machine learning	Process / pipeline
Jaar van ontstaan	1992–2011	1975 (foundational); 2012 (ML standard)
Grondlegger	MacKay, D.J.C.; Houlsby, N. et al.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Type	Active learning with Bayesian uncertainty	Sequential model-based black-box optimization
Oorspronkelijke bron	Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Aliassen	BAL, Bayesian optimal experimental design for ML, BALD (Bayesian Active Learning by Disagreement), probabilistic active learning	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Verwant	6	2
Samenvatting	Bayesian Active Learning (BAL) combines a probabilistic model with an active query strategy to identify the unlabeled examples that, once labeled, would most reduce model uncertainty. Instead of labeling data at random, BAL guides an oracle — typically a human annotator — toward the points where labeling will provide the greatest information gain, making it highly label-efficient.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.