Learning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
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Association for the Advancement of Artificial Intelligence
2024
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| _version_ | 1797113180762144768 |
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| author | Hocquette, C Niskanen, A Järvisalo, M Cropper, A |
| author_facet | Hocquette, C Niskanen, A Järvisalo, M Cropper, A |
| author_sort | Hocquette, C |
| collection | OXFORD |
| description | Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise. |
| first_indexed | 2024-03-07T08:23:49Z |
| format | Conference item |
| id | oxford-uuid:dc7892b5-914d-4c94-8d1e-34ba3b2f48aa |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-04-09T03:59:13Z |
| publishDate | 2024 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | oxford-uuid:dc7892b5-914d-4c94-8d1e-34ba3b2f48aa2024-04-08T11:32:41ZLearning MDL logic programs from noisy dataConference itemhttp://purl.org/coar/resource_type/c_5794uuid:dc7892b5-914d-4c94-8d1e-34ba3b2f48aaEnglishSymplectic ElementsAssociation for the Advancement of Artificial Intelligence2024Hocquette, CNiskanen, AJärvisalo, MCropper, AMany inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise. |
| spellingShingle | Hocquette, C Niskanen, A Järvisalo, M Cropper, A Learning MDL logic programs from noisy data |
| title | Learning MDL logic programs from noisy data |
| title_full | Learning MDL logic programs from noisy data |
| title_fullStr | Learning MDL logic programs from noisy data |
| title_full_unstemmed | Learning MDL logic programs from noisy data |
| title_short | Learning MDL logic programs from noisy data |
| title_sort | learning mdl logic programs from noisy data |
| work_keys_str_mv | AT hocquettec learningmdllogicprogramsfromnoisydata AT niskanena learningmdllogicprogramsfromnoisydata AT jarvisalom learningmdllogicprogramsfromnoisydata AT croppera learningmdllogicprogramsfromnoisydata |