Andrew Cropper

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I am a junior research fellow at the University of Oxford.

I design machine learning algorithms that learn to write computer programs. I focus on inductive logic programming (ILP), which combines machine learning and mathematical logic.

I developed the ILP systems Metagol and Popper.

Email CV

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Publications

DBLP Scholar

Journals

1. Inductive general game playing
   A. Cropper, R. Evans, and M. Law
   Machine learning 2020
   slides code dataset
2. Logical minimisation of metarules
   A. Cropper and S. Tourret
   Machine learning 2020
   slides code
3. Learning higher-order logic programs
   A. Cropper, R. Morel, and S.H. Muggleton
   Machine learning 2020
   slides code
4. Learning efficient logic programs
   A. Cropper and S.H. Muggleton
   Machine learning 2019
   slides code

Conferences

1. Learning large logic programs by going beyond entailment
   A. Cropper and S. Dumančić
   IJCAI 2020
2. Turning 30: new ideas in inductive logic programming
   A. Cropper, Dumančić, and S.H. Muggleton
   IJCAI 2020
3. Forgetting to learn logic programs
   A. Cropper
   AAAI 2020
   slides code
4. Learning higher-order programs through predicate invention
   A. Cropper, R. Morel, and S.H. Muggleton
   AAAI 2020
   slides
5. Playgol: learning programs through play
   A. Cropper
   IJCAI 2019
   slides code
6. SLD-resolution reduction of second-order horn fragments
   S. Tourret and A. Cropper
   JELIA 2019
7. Typed meta-interpretive learning of logic programs
   R. Morel, A. Cropper, and L. Ong
   JELIA 2019
   slides code
8. Derivation reduction of metarules in meta-interpretive learning
   A. Cropper and S. Tourret
   ILP 2018
   slides code
9. Learning higher-order logic programs through abstraction and invention
   A. Cropper and S.H. Muggleton
   IJCAI 2016
   slides code
10. Logic-based inductive synthesis of efficient programs
    A. Cropper
    IJCAI 2016
    slides
11. Learning efficient logical robot strategies involving composable objects
    A. Cropper and S.H. Muggleton
    IJCAI 2015
    slides code
12. Learning efficient logic programs
    A. Cropper
    IJCAI 2015
    slides
13. Meta-interpretive learning of data transformation programs
    A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton
    ILP 2015
    slides code
14. Typed meta-interpretive learning for proof strategies
    C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
    ILP 2015
15. Can predicate invention compensate for incomplete background knowledge?
    A. Cropper and S.H. Muggleton
    SCAI 2015
    slides
16. Logical minimisation of meta-rules within meta-interpretive learning
    A. Cropper and S.H. Muggleton
    ILP 2014
    slides

Workshops

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

Others

1. Learning programs by learning from failures
   A. Cropper and R. Morel
   Under review
   
2. Knowledge refactoring for program induction
   S. Dumančić and A. Cropper
   In preparation.
3. Inductive logic programming at 30: a new introduction
   A. Cropper and S. Dumančić.
   In preparation.

Theses

1. Efficiently learning efficient programs
   A. Cropper. PhD thesis, Imperial College London, 2017.
2. Modelling stock volume using Twitter
   A. Cropper. MSc thesis, University of Oxford, 2011.

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Talks

• Learning higher-order logic programs KU Leuven, 2019.
• Inductive general game playing KU Leuven, 2019.
• Playgol: learning programs through play KU Leuven, 2019.
• Inductive general game playing MIT, 2019.
• Playgol: learning programs through play MIT, 2019.
• Playgol: learning programs through play Machine intelligence 21, 2019.
• Inductive general game playing Dagstuhl, 2019.
• Playgol: learning programs through play Dagstuhl, 2019.
• Learning algorithms using logic University of Oxford, 2019.
• Learning efficient logic programs MIT, 2018.
• Learning efficient logic programs Dagstuhl, 2017.
• Learning higher-order logic programs Dagstuhl, 2017.
• Learning efficient logic programs Machine intelligence 20, 2016.
• Logic-based learning of programs. UC Berkeley, 2016.
• Metagol. Dagstuhl, 2015.
• Predicate invention in meta-interpretive learning. Wakayama University, Japan, 2014.