| Arxiv 2024 |
Relational decomposition for program synthesis
Hocquette and Cropper |
| Arxiv 2024 |
Scalable knowledge refactoring using constrained optimisation
Liu, Cerna, Gouveia, and Cropper |
| Nature Comms 2024 |
Symbolic metaprogram search improves learning efficiency and explains rule learning in humans
Rule, Piantadosi, Cropper, Ellis, Nye, and Tenenbaum |
| ECAI 2024 |
Learning logic programs by finding minimal unsatisfiable subprograms
Cropper and Hocquette |
| IJCAI 2024 |
Learning big logical rules by joining small rules
Hocquette, Niskanen, Morel, Järvisalo, and Cropper |
| IJCAI 2024 |
Learning logic programs by discovering higher-order abstractions
Hocquette, Dumančić, and Cropper |
| AAAI 2024 |
Learning MDL logic programs from noisy data
Hocquette, Niskanen, Järvisalo, and Cropper |
| AAAI 2024 |
Generalisation through negation and predicate invention
Cerna and Cropper |
| ECAI 2023 |
Learning logic programs by combining programs
Cropper and Hocquette |
| AAAI 2023 |
Relational program synthesis with numerical reasoning
Hocquette and Cropper |
| AAAI 2023 |
Learning logic programs by discovering where not to search
Cropper and Hocquette |
| AAAI 2023 |
The automatic computer scientist
Cropper |
| MLJ 2023 |
Learning programs by explaining failures
Morel and Cropper |
| MLJ 2023 |
Learning programs with magic values
Hocquette and Cropper |
| AAAI 2022 |
Learning logic programs through divide, constrain, and conquer
Cropper |
| JAIR 2022 |
Inductive logic programming at 30: a new introduction
Cropper and Dumančić |
| MLJ 2022 |
Inductive logic programming at 30
Cropper, Dumančić, Evans, and Muggleton |
| AAAI 2021 |
Knowledge refactoring for inductive program synthesis
Dumančić, Guns, and Cropper |
| MLJ 2021 |
Learning programs by learning from failures
Cropper and Morel |
| IJCAI 2020 |
Learning large logic programs by going beyond entailment
Cropper and Dumančić |
| IJCAI 2020 |
Turning 30: new ideas in inductive logic programming
Cropper, Dumančić, and Muggleton |
| AAAI 2020 |
Forgetting to learn logic programs
Cropper |
| AAAI 2020 |
Learning higher-order programs through predicate invention
Cropper, Morel, and Muggleton |
| MLJ 2020 |
Inductive general game playing
Cropper, Evans, and Law |
| MLJ 2020 |
Logical reduction of metarules
Cropper and Tourret |
| MLJ 2020 |
Learning higher-order logic programs
Cropper, Morel, and Muggleton |
| IJCAI 2019 |
Playgol: learning programs through play
Cropper |
| JELIA 2019 |
SLD-resolution reduction of second-order Horn fragments
Tourret and Cropper |
| JELIA 2019 |
Typed meta-interpretive learning of logic programs
Morel, Cropper, and Ong |
| MLJ 2019 |
Learning efficient logic programs
Cropper and Muggleton |
| ILP 2018 |
Derivation reduction of metarules in meta-interpretive learning
Cropper and Tourret |
| IJCAI 2016 |
Learning higher-order logic programs through abstraction and invention
Cropper and Muggleton |
| IJCAI 2016 |
Logic-based inductive synthesis of efficient programs
Cropper |
| IJCAI 2015 |
Learning efficient logical robot strategies involving composable objects
Cropper and Muggleton |
| IJCAI 2015 |
Learning efficient logic programs
Cropper |
| ILP 2015 |
Meta-interpretive learning of data transformation programs
Cropper, Tamaddoni-Nezhad, and Muggleton |
| ILP 2015 |
Typed meta-interpretive learning for proof strategies
Farquhar, Grov, Cropper, Muggleton, and Bundy |
| SCAI 2015 |
Can predicate invention compensate for incomplete background knowledge?
Cropper and Muggleton |
| ILP 2014 |
Logical minimisation of meta-rules within meta-interpretive learning
Cropper and Muggleton |
