Bayesian Inference
Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
Maximizing acquisition functions for Bayesian optimization
James Wilson, Frank Hutter, Marc Deisenroth
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Riemannian Laplace approximations for Bayesian neural networks
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However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates.
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Robust Learning of Fixed-Structure Bayesian Networks
Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
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Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling
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Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski, Jonathan W. Pillow
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Predictive Uncertainty Estimation via Prior Networks
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