Andrew Cropper

I am a junior research fellow at the University of Oxford. I work on inductive logic programming (ILP), a form of machine learning which learns logic programs from data. I maintain the ILP system Metagol.

CV Email

Publications

DBLP Google scholar

Journals

• A. Cropper and S.H. Muggleton. Learning efficient logic programs. Machine learning (2018). https://doi.org/10.1007/s10994-018-5712-6 Code

Conferences

• A. Cropper and S.H. Muggleton. Learning higher-order logic programs through abstraction and invention. IJCAI 2016. Slides Code
• A. Cropper and S.H. Muggleton. Learning efficient logical robot strategies involving composable objects. IJCAI 2015. Slides Code
• A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton. Meta-interpretive learning of data transformation programs. ILP 2015. Slides Code
• C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy. Typed meta-interpretive learning for proof strategies. ILP 2015.
• A. Cropper and S.H. Muggleton. Can predicate invention compensate for incomplete background knowledge? SCAI 2015. Slides
• A. Cropper and S.H. Muggleton. Logical minimisation of meta-rules within meta-interpretive learning. ILP 2014. Slides

Workshops

• S. Tourret and A. Cropper. SLD-resolution reduction of second-order Horn fragments. Termgraph 2018.
• A. Cropper. Identifying and inferring objects from textual descriptions of scenes from books. ICCSW 2014. Slides

Extended abstracts

• A. Cropper. Logic-based inductive synthesis of efficient programs. IJCAI 2016. Slides
• A. Cropper. Learning efficient logic programs. IJCAI 2015. Slides

• A. Cropper. Efficiently learning efficient programs. PhD thesis, Imperial College London, 2017.
• A. Cropper. Modelling stock volume using Twitter. Master's thesis, University of Oxford, 2011.

Talks

• Learning efficient logic programs, Workshop on approaches and Applications of inductive programming, Dagstuhl, Germany, 2017.
• Learning higher-order logic programs, Workshop on approaches and Applications of inductive programming, Dagstuhl, Germany, 2017.
• Learning efficient logic programs, Machine intelligence 20 workshop on human-like computing, London, UK, 2016.
• Logic-based learning of programs from input/output examples, UC Berkeley, USA, July 2016.
• Metagol, Workshop on approaches and Applications of inductive programming, Dagstuhl, Germany, 2015.
• Predicate invention in meta-interpretive learning, Meeting on abductive and inductive reasoning, Wakayama University, Japan, 2014.