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Causal variables from reinforcement learning using generalized Bellman equations
Many open problems in machine learning are intrinsically related to caus...
10/29/2020 ∙ by Tue Herlau, et al. ∙
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Bayesian Dropout
Dropout has recently emerged as a powerful and simple method for trainin...
08/12/2015 ∙ by Tue Herlau, et al. ∙
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Efficient inference of overlapping communities in complex networks
We discuss two views on extending existing methods for complex network m...
11/28/2014 ∙ by Bjarne Ørum Fruergaard, et al. ∙
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Adaptive Reconfiguration Moves for Dirichlet Mixtures
Bayesian mixture models are widely applied for unsupervised learning and...
05/31/2014 ∙ by Tue Herlau, et al. ∙
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The Infinite Degree Corrected Stochastic Block Model
In Stochastic blockmodels, which are among the most prominent statistica...
11/11/2013 ∙ by Tue Herlau, et al. ∙
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