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Found 3 papers, 1 papers with code
Explainable Models via Compression of Tree Ensembles
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs).
Neural Networks for Relational Data
While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domains such as those arising in applications like social network analysis.
Probabilistic Relational Planning with First Order Decision Diagrams
Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation.
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