1 citations · 2 across the 6 of their papers we have counts for
6 papers
Symbolic Snapshot Ensembles
Mingyue Liu, Andrew Cropper
Inductive logic programming (ILP) is a form of logical machine learning. Most ILP algorithms learn a single hypothesis from a single training run. Ensemble methods train an ILP alg…
Symmetry breaking for inductive logic programming
Andrew Cropper, David M. Cerna, Matti Järvisalo
The goal of inductive logic programming is to search for a hypothesis that generalises training data and background knowledge. The challenge is searching vast hypothesis spaces, wh…
Learning Logical Rules using Minimum Message Length
Ruben Sharma, Sebastijan Dumančić, Ross D. King +1
Unifying probabilistic and logical learning is a key challenge in AI. We introduce a Bayesian inductive logic programming approach that learns minimum message length hypotheses fro…
Honey, I shrunk the hypothesis space (through logical preprocessing)
Andrew Cropper, Filipe Gouveia, David M. Cerna
Inductive logic programming (ILP) is a form of logical machine learning. The goal is to search a hypothesis space for a hypothesis that generalises training examples and background…
Efficient rule induction by ignoring pointless rules
Andrew Cropper, David M. Cerna
The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that ident…
Scalable Knowledge Refactoring using Constrained Optimisation
Minghao Liu, David M. Cerna, Filipe Gouveia +1
Knowledge refactoring compresses a logic program by introducing new rules. Current approaches struggle to scale to large programs. To overcome this limitation, we introduce a const…