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Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data.
Sample estimate of the entropy of a random vector
Kozachenko, LF and Leonenko, Nikolai N · 1987
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Information-based objective functions for active data selection
MacKay, David JC · 1992
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Risk-sensitive reinforcement learning
Mihatsch, Oliver and Neuneier, Ralph · 2002
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Estimating mutual information
Kraskov, Alexander, Stögbauer, Harald, and Grassberger, Peter · 2004
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Uncertainty propagation for quality assurance in reinforcement learning
Schneegass, Daniel, Udluft, Steffen, and Martinetz, Thomas · 2008
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A survey on policy search for robotics
Deisenroth, Marc Peter, Neumann, Gerhard, Peters, Jan, et al · 2013
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Reinforcement learning with misspecified model classes
Joseph, Joshua, Geramifard, Alborz, Roberts, John W, How, Jonathan P, and Roy, Nicholas · 2013
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Weight uncertainty in neural networks
Blundell, Charles, Cornebise, Julien, Kavukcuoglu, Koray, and Wierstra, Daan · 2015
Cited alongside, same era.
A comprehensive survey on safe reinforcement learning
García, Javier and Fernández, Fernando · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, José Miguel and Adams, Ryan P · 2015
Cited alongside, same era.
Learning and policy search in stochastic dynamical systems with bayesian neural networks
Depeweg, Stefan, Hernández-Lobato, José Miguel, Doshi-Velez, Finale, and Udluft, Steffen · 2016
Cited alongside, same era.
Improving pilco with bayesian neural network dynamics models
Gal, Yarin, McAllister, Rowan Thomas, and Rasmussen, Carl Edward · 2016
Introduction to the” industrial benchmark”
Hein, Daniel, Hentschel, Alexander, Sterzing, Volkmar, Tokic, Michel, and Udluft, Steffen · 2016
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Black-box α \alpha -divergence minimization
Hernández-Lobato, José Miguel, Li, Yingzhen, Rowland, Mark, Hernández-Lobato, Daniel, Bui, Thang, and Turner, Richard E · 2016
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VIME: Variational information maximizing exploration
Houthooft, Rein, Chen, Xi, Duan, Yan, Schulman, John, De Turck, Filip, and Abbeel, Pieter · 2016
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, Alex and Gal, Yarin · 2017
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Maddison, Chris J, Lawson, Dieterich, Tucker, George, Heess, Nicolas, Doucet, Arnaud, Mnih, Andriy, and Teh, Yee Whye · 2017
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Cited alongside, same era.
Breaking the bandwidth barrier: Geometrical adaptive entropy estimation
Gao, Weihao, Oh, Sewoong, and Viswanath, Pramod · 2016
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Learning multimodal transition dynamics for model-based reinforcement learning
Moerland, Thomas M, Broekens, Joost, and Jonker, Catholijn M · 2017
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