Tsamardinos, I., Borboudakis, G.: Permutation testing improves bayesian network learning. In: Balca´zar, J., Bonchi, F., Gionis, A., Sebag, M. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 6323, pp. 322-337. Springer Berlin,...
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Explore related subjects Discover the latest articles and news from researchers in related subjects, suggested using machine learning. Bayesian Network Bayesian Inference Statistical Learning Machine Learning Learning algorithms Probabilistic data networks ...
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Machine Learning Aims and scope Submit manuscript François Petitjean, Wray Buntine, Geoffrey I. Webb & Nayyar Zaidi 3622 Accesses 17 Citations Explore all metrics Abstract This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), ...
& Lee, J. Scale mixtures of neural network Gaussian processes. In International Conference on Learning Representations (ICLR, 2022). Aitchison, L. Why bigger is not always better: on finite and infinite neural networks. In Proc. 37th International Conference on Machine Learning (eds Daumé, H....
In recent years, neural network is widely applied to variable estimation in complex systems. Neural network is an end-to-end system that mimics the human brain and tries to learn complex representation within the dataset to provide an output. Similar to conventional machine learning, deep neural ...
Bishop CM: Pattern recognition and machine learning. Springer; 2006. Google Scholar Pearl J: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann; 1997. Google Scholar Ghahramani Z: Learning dynamic Bayesian networks. Lect Notes Comp Sci 1998, 1387: 168–...
In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, ...