Advanced statistical modeling and knowledge representation techniques for a newly
emerging area of machine learning and probabilistic reasoning; includes introductory …

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-
cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes …

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Standard inductive learning requires that training and test instances come from the same
distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test …

Relational learning can be described as the task of learning first-order logic rules from
examples. It has enabled a number of new machine learning applications, eg graph mining …

Precision-recall (PR) curves and the areas under them are widely used to summarize
machine learning results, especially for data sets exhibiting class skew. They are often used …

Markov logic networks (MLNs) are an expressive representation for statistical relational
learning that generalizes both first-order logic and graphical models. Existing methods for …

Learning first-order logic programs from relational facts yields intuitive insights into the data.
Inductive logic programming (ILP) models are effective in learning first-order logic programs …

Relational data representations have become an increasingly important topic due to the
recent proliferation of network datasets (eg, social, biological, information networks) and a …

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical
models to perform inference more efficiently. More specifically, they identify groups of …