20 citations · 43 across the 10 of their papers we have counts for
10 papers
Probability trees and the value of a single intervention
Tue Herlau
The most fundamental problem in statistical causality is determining causal relationships from limited data. Probability trees, which combine prior causal structures with Bayesian…
Moral reinforcement learning using actual causation
Tue Herlau
Reinforcement learning systems will to a greater and greater extent make decisions that significantly impact the well-being of humans, and it is therefore essential that these syst…
Active learning of causal probability trees
Tue Herlau
The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide…
Reinforcement Learning of Causal Variables Using Mediation Analysis
Tue Herlau, Rasmus Larsen
Many open problems in machine learning are intrinsically related to causality, however, the use of causal analysis in machine learning is still in its early stage. Within a general…
Bayesian Dropout
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grou…
Completely random measures for modelling block-structured networks
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the s…
Efficient inference of overlapping communities in complex networks
Bjarne Ørum Fruergaard, Tue Herlau
We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networ…
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Tue Herlau, Morten Mørup, Yee Whye Teh +1
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widel…
Nonparametric Bayesian models of hierarchical structure in complex networks
Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can…
The Infinite Degree Corrected Stochastic Block Model
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statisticall…