Andrew Cropper

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.

I am always looking for collaborators so please contact me if you want to work on ILP.

Email CV

Publications

DBLP Scholar

Journals

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

Conferences

  1. Knowledge refactoring for inductive program synthesis
    S. Dumančić, T. Guns, and A. Cropper
    AAAI 2021
  2. Learning large logic programs by going beyond entailment
    A. Cropper and S. Dumančić
    IJCAI 2020
  3. Turning 30: new ideas in inductive logic programming
    A. Cropper, S. Dumančić, and S.H. Muggleton
    IJCAI 2020
  4. Forgetting to learn logic programs
    A. Cropper
    AAAI 2020
    slides code
  5. Learning higher-order programs through predicate invention
    A. Cropper, R. Morel, and S.H. Muggleton
    AAAI 2020
    slides
  6. Playgol: learning programs through play
    A. Cropper
    IJCAI 2019
    slides code
  7. SLD-resolution reduction of second-order horn fragments
    S. Tourret and A. Cropper
    JELIA 2019
  8. Typed meta-interpretive learning of logic programs
    R. Morel, A. Cropper, and L. Ong
    JELIA 2019
    slides code
  9. Derivation reduction of metarules in meta-interpretive learning
    A. Cropper and S. Tourret
    ILP 2018
    slides code
  10. Learning higher-order logic programs through abstraction and invention
    A. Cropper and S.H. Muggleton
    IJCAI 2016
    slides code
  11. Logic-based inductive synthesis of efficient programs
    A. Cropper
    IJCAI 2016
    slides
  12. Learning efficient logical robot strategies involving composable objects
    A. Cropper and S.H. Muggleton
    IJCAI 2015
    slides code
  13. Learning efficient logic programs
    A. Cropper
    IJCAI 2015
    slides
  14. Meta-interpretive learning of data transformation programs
    A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton
    ILP 2015
    slides code
  15. Typed meta-interpretive learning for proof strategies
    C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
    ILP 2015
  16. Can predicate invention compensate for incomplete background knowledge?
    A. Cropper and S.H. Muggleton
    SCAI 2015
    slides
  17. 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

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.

Talks