mlsanityData to 14 Oct 2026

Learning MDL logic programs from noisy data

arXiv 2308.09393 ↗First posted 18 Aug 2023cs.LG cs.LOcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 15 of its 63 references that are in the map. First posted on arXiv 18 Aug 2023.

Builds on

15 of 63 references.

References · 15

  1. 2017Learning Explanatory Rules from Noisy Datacited by 211
  2. 2020Inductive logic programming at 30: a new introductioncited by 85
  3. 2020Learning programs by learning from failurescited by 73
  4. 2014Clingo = ASP + Control: Preliminary Reportcited by 56
  5. 2021Inductive logic programming at 30cited by 47
  6. 2020Abductive Knowledge Induction From Raw Datacited by 38
  7. 2019Making sense of sensory inputcited by 29
  8. 2019Synthesizing Datalog Programs Using Numerical Relaxationcited by 23
  9. 2018Inductive Learning of Answer Set Programs from Noisy Examplescited by 22
  10. 2019Inductive general game playingcited by 18
  11. 2021Learning Higher-Order Programs without Meta-Interpretive Learningcited by 14
  12. 2022Learning logic programs by combining programscited by 7
3 more
  1. 2021Top Program Construction and Reduction for polynomial time Meta-Interpretive Learningcited by 6
  2. 2019Towards meta-interpretive learning of programming language semanticscited by 5
  3. 2021Learning Logic Programs From Noisy Failurescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 9; 2026: 1.

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  1. 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomycited by 16
  2. 2024LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulationcited by 8
  3. 2024Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxationcited by 5
  4. 2024From Code to Play: Benchmarking Program Search for Games Using Large Language Modelscited by 5
  5. 2024Learning big logical rules by joining small rulescited by 4
  6. 2025Hypothesis Generation via LLM-Automated Language Bias for ILPcited by 2
  7. 2026Differentiable Rule Induction from Raw Sequence Inputscited by 2
  8. 2025Learning Logical Rules using Minimum Message Lengthcited by 1
  9. 2025GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoningcited by 1
  10. 2025Efficient rule induction by ignoring pointless rulescited by 0
  11. 2025An Empirical Comparison of Cost Functions in Inductive Logic Programmingcited by 0
  12. 2025Honey, I shrunk the hypothesis space (through logical preprocessing)cited by 0
3 more
  1. 2025Symmetry breaking for inductive logic programmingcited by 0
  2. 2025RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Modelscited by 0
  3. 2025Compressing Chemistry Reveals Functional Groupscited by 0