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Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data.
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Estimating mutual information
Kraskov, A., Stögbauer, H., and Grassberger, P · 2004
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Optimistic active-learning using mutual information
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Larsen, T. J. and Hansen, A. M · 2007
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Safe exploration for reinforcement learning
Hans, A., Schneegaß, D., Schäfer, A. M., and Udluft, S · 2008
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Aleatory or epistemic? does it matter?
Der Kiureghian, A. and Ditlevsen, O · 2009
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Efficient uncertainty propagation for reinforcement learning with limited data
Hans, A. and Udluft, S · 2009
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Pilco: A model-based and data-efficient approach to policy search
Deisenroth, M. and Rasmussen, C. E · 2011
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Planning to be surprised: Optimal bayesian exploration in dynamic environments
Sun, Y., Gomez, F., and Schmidhuber, J · 2011
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Safe exploration of state and action spaces in reinforcement learning
Garcia, J. and Fernández, F · 2012
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Collaborative gaussian processes for preference learning
Houlsby, N., Huszar, F., Ghahramani, Z., and Hernández-Lobato, J. M · 2012
Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
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Comparison of measured and simulated loads for the siemens swt2.3 operating in wake conditions at the lillgrund wind farm using hawc2 and the dynamic wake meander model
Larsen, T. J., Larsen, G., Madsen, H. A., Thomsen, K., and Pedersen, S. M · 2015
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Learning and policy search in stochastic dynamical systems with bayesian neural networks
Depeweg, S., Hernández-Lobato, J. M., Doshi-Velez, F., and Udluft, S · 2016
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Uncertainty in deep learning
Gal, Y · 2016
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Breaking the bandwidth barrier: Geometrical adaptive entropy estimation
Gao, W., Oh, S., and Viswanath, P · 2016
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Black-box
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Batch reinforcement learning
Lange, S., Gabel, T., and Riedmiller, M · 2012
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Active learning
Settles, B · 2012
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An information-theoretic approach to curiosity-driven reinforcement learning
Still, S. and Precup, D · 2012
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Reinforcement learning with misspecified model classes
Joseph, J., Geramifard, A., Roberts, J. W., How, J. P., and Roy, N · 2013
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Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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García, J. and Fernández, F · 2015
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Hernández-Lobato, J. M., Li, Y., Rowland, M., Hernández-Lobato, D., Bui, T., and Turner, R. E · 2016
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VIME: Variational information maximizing exploration
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Bayesian Learning for Data-Efficient Control
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Safe model-based reinforcement learning with stability guarantees
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Hein, D., Depeweg, S., Tokic, M., Udluft, S., Hentschel, A., Runkler, T. A., and Sterzing, V · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
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Maddison, C. J., Lawson, D., Tucker, G., Heess, N., Doucet, A., Mnih, A., and Teh, Y. W · 2017
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