8 citations · 8 across the 3 of their papers we have counts for
12 papers
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans +1
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP t…
Feature Interactions in XGBoost
Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel
In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results…
Knowledge Refactoring for Inductive Program Synthesis
Sebastijan Dumancic, Tias Guns, Andrew Cropper
Humans constantly restructure knowledge to use it more efficiently. Our goal is to give a machine learning system similar abilities so that it can learn more efficiently. We introd…
Neural Probabilistic Logic Programming in DeepProbLog
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learni…
Learning Relational Representations with Auto-encoding Logic Programs
Sebastijan Dumancic, Tias Guns, Wannes Meert +1
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order lo…
Learning Sequence Encoders for Temporal Knowledge Graph Completion
Alberto García-Durán, Sebastijan Dumančić, Mathias Niepert
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entit…