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Found 11 papers, 0 papers with code
Probability trees and the value of a single intervention
The most fundamental problem in statistical causality is determining causal relationships from limited data.
Moral reinforcement learning using actual causation
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 systems make decisions that conform to our expectations of morally good behavior.
Active learning of causal probability trees
Here we present a Bayesian method for learning probability trees from a combination of interventional and observational data.
Reinforcement Learning of Causal Variables Using Mediation Analysis
To our knowledge, this is the first attempt to apply causal analysis in a reinforcement learning setting without strict restrictions on the number of states.
Completely random measures for modelling block-structured sparse networks
Statistical methods for network data often parameterize the edge-probability by attributing latent traits such as block structure to the vertices and assume exchangeability in the sense of the Aldous-Hoover representation theorem.
Bayesian Dropout
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons.
Completely random measures for modelling block-structured networks
Recently Caron and Fox (2014) proposed the use of a different notion of exchangeability due to Kallenberg (2009) and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure.
Efficient inference of overlapping communities in complex networks
We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively.
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis.
The Infinite Degree Corrected Stochastic Block Model
On synthetic data we demonstrate that including the degree correction yields better performance both on recovering the true group structure and predicting missing links when degree heterogeneity is present, whereas performance is on par for data with no degree heterogeneity within clusters.
Nonparametric Bayesian models of hierarchical structure in complex networks
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain.
