ML Anthology

Learning MDL Logic Programs from Noisy Data

Céline Hocquette, Andreas Niskanen, Matti Järvisalo, Andrew Cropper
AAAI 2024 pp. 10553-10561
doi:10.1609/AAAI.V38I9.28925 /aaai/2024/hocquette2024aaai-learning/

Abstract

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.

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Text

Hocquette et al. "Learning MDL Logic Programs from Noisy Data." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28925

Markdown

[Hocquette et al. "Learning MDL Logic Programs from Noisy Data." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/hocquette2024aaai-learning/) doi:10.1609/AAAI.V38I9.28925

BibTeX

@inproceedings{hocquette2024aaai-learning,
  title     = {{Learning MDL Logic Programs from Noisy Data}},
  author    = {Hocquette, Céline and Niskanen, Andreas and Järvisalo, Matti and Cropper, Andrew},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {10553-10561},
  doi       = {10.1609/AAAI.V38I9.28925},
  url       = {https://mlanthology.org/aaai/2024/hocquette2024aaai-learning/}
}