Andrew Cropper
I work on combining logic and machine learning (inductive logic programming) at the University of Oxford. I run the logic and learning (LoL) group and the Automatic Computer Scientist (AutoCS) project. I develop the ILP system Popper.

I am recruiting multiple 1-3 year postdocs to work on the AutoCS project. I am looking for at least one expert in constraint satisfaction (SAT/SMT/ASP/CSP). If interested, please email me.

Email CV

Publications

DBLP Scholar

Journals

  1. Learning programs with magic values
    C. Hocquette and A. Cropper
    Machine learning 2022
    slides code
  2. Inductive logic programming at 30: a new introduction
    A. Cropper and S. Dumančić
    JAIR 2022
  3. Inductive logic programming at 30
    A. Cropper, S. Dumančić, R. Evans, and S.H. Muggleton
    Machine learning 2022
  4. Learning programs by learning from failures
    A. Cropper and R. Morel
    Machine learning 2021
  5. Inductive general game playing
    A. Cropper, R. Evans, and M. Law
    Machine learning 2020
    slides code dataset
  6. Logical reduction of metarules
    A. Cropper and S. Tourret
    Machine learning 2020
    slides code
  7. Learning higher-order logic programs
    A. Cropper, R. Morel, and S.H. Muggleton
    Machine learning 2020
    slides code
  8. Learning efficient logic programs
    A. Cropper and S.H. Muggleton
    Machine learning 2019
    slides code

Conferences

  1. Relational program synthesis with numerical reasoning
    C. Hocquette and A. Cropper
    AAAI 2023
  2. Learning logic programs by discovering where not to search
    A. Cropper and C. Hocquette
    AAAI 2023
  3. Learning logic programs through divide, constrain, and conquer
    A. Cropper
    AAAI 2022
    slides code
  4. Knowledge refactoring for inductive program synthesis
    S. Dumančić, T. Guns, and A. Cropper
    AAAI 2021
    code
  5. Learning large logic programs by going beyond entailment
    A. Cropper and S. Dumančić
    IJCAI 2020
  6. Turning 30: new ideas in inductive logic programming
    A. Cropper, S. Dumančić, and S.H. Muggleton
    IJCAI 2020
  7. Forgetting to learn logic programs
    A. Cropper
    AAAI 2020
    slides code
  8. Learning higher-order programs through predicate invention
    A. Cropper, R. Morel, and S.H. Muggleton
    AAAI 2020
    slides
  9. Playgol: learning programs through play
    A. Cropper
    IJCAI 2019
    slides code
  10. SLD-resolution reduction of second-order horn fragments
    S. Tourret and A. Cropper
    JELIA 2019
  11. Typed meta-interpretive learning of logic programs
    R. Morel, A. Cropper, and L. Ong
    JELIA 2019
    slides code
  12. Derivation reduction of metarules in meta-interpretive learning
    A. Cropper and S. Tourret
    ILP 2018
    slides code
  13. Learning higher-order logic programs through abstraction and invention
    A. Cropper and S.H. Muggleton
    IJCAI 2016
    slides code
  14. Logic-based inductive synthesis of efficient programs
    A. Cropper
    IJCAI 2016
    slides
  15. Learning efficient logical robot strategies involving composable objects
    A. Cropper and S.H. Muggleton
    IJCAI 2015
    slides code
  16. Learning efficient logic programs
    A. Cropper
    IJCAI 2015
    slides
  17. Meta-interpretive learning of data transformation programs
    A. Cropper, A. Tamaddoni-Nezhad, and S.H. Muggleton
    ILP 2015
    slides code
  18. Typed meta-interpretive learning for proof strategies
    C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
    ILP 2015
  19. Can predicate invention compensate for incomplete background knowledge?
    A. Cropper and S.H. Muggleton
    SCAI 2015
    slides
  20. Logical minimisation of meta-rules within meta-interpretive learning
    A. Cropper and S.H. Muggleton
    ILP 2014
    slides

Theses

  1. Efficiently learning efficient programs
    A. Cropper. PhD thesis, Imperial College London, 2017

Selected talks