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

Learning programs by learning from failures
A. Cropper and R. Morel
Machine learning (to appear)

Inductive general game playing
A. Cropper, R. Evans, and M. Law
Machine learning 2020
slides
code
dataset

Logical minimisation of metarules
A. Cropper and S. Tourret
Machine learning 2020
slides
code

Learning higherorder logic programs
A. Cropper, R. Morel, and S.H. Muggleton
Machine learning 2020
slides
code

Learning efficient logic programs
A. Cropper and S.H. Muggleton
Machine learning 2019
slides
code
Conferences

Knowledge refactoring for inductive program synthesis
S. Dumančić, T. Guns, and A. Cropper
AAAI 2021

Learning large logic programs by going beyond entailment
A. Cropper and S. Dumančić
IJCAI 2020

Turning 30: new ideas in inductive logic programming
A. Cropper, S. Dumančić, and S.H. Muggleton
IJCAI 2020

Forgetting to learn logic programs
A. Cropper
AAAI 2020
slides
code

Learning higherorder programs through predicate invention
A. Cropper, R. Morel, and S.H. Muggleton
AAAI 2020
slides

Playgol: learning programs through play
A. Cropper
IJCAI 2019
slides
code

SLDresolution reduction of secondorder horn fragments
S. Tourret and A. Cropper
JELIA 2019

Typed metainterpretive learning of logic programs
R. Morel, A. Cropper, and L. Ong
JELIA 2019
slides
code

Derivation reduction of metarules in metainterpretive learning
A. Cropper and S. Tourret
ILP 2018
slides
code

Learning higherorder logic programs through abstraction and invention
A. Cropper and S.H. Muggleton
IJCAI 2016
slides
code

Logicbased inductive synthesis of efficient programs
A. Cropper
IJCAI 2016
slides

Learning efficient logical robot strategies involving composable objects
A. Cropper and S.H. Muggleton
IJCAI 2015
slides
code

Learning efficient logic programs
A. Cropper
IJCAI 2015
slides

Metainterpretive learning of data transformation programs
A. Cropper, A. TamaddoniNezhad, and S.H. Muggleton
ILP 2015
slides
code

Typed metainterpretive learning for proof strategies
C. Farquhar, G. Grov, A. Cropper, S.H. Muggleton, and A. Bundy
ILP 2015

Can predicate invention compensate for incomplete background knowledge?
A. Cropper and S.H. Muggleton
SCAI 2015
slides

Logical minimisation of metarules within metainterpretive learning
A. Cropper and S.H. Muggleton
ILP 2014
slides
Workshops

SLDresolution reduction of secondorder Horn fragments
S. Tourret and A. Cropper
Termgraph 2018

Identifying and inferring objects from textual descriptions of scenes from books
A. Cropper
ICCSW 2014
slides
Theses

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

Modelling stock volume using Twitter
A. Cropper.
MSc thesis, University of Oxford, 2011.
Talks
 Learning programs from failures
MIT, 2020
 Learning higherorder 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 higherorder logic programs
Dagstuhl, 2017
 Learning efficient logic programs
Machine intelligence 20, 2016
 Logicbased learning of programs.
UC Berkeley, 2016
 Metagol.
Dagstuhl, 2015
 Predicate invention in metainterpretive learning.
Wakayama University, Japan, 2014