mlsanityData to 14 Oct 2026

Learning Higher-Order Programs without Meta-Interpretive Learning

arXiv 2112.14603 ↗First posted 29 Dec 2021cs.AI cs.LOcited by 14

Not on the map. Why?

Abstract (arXiv)

Learning complex programs through inductive logic programming (ILP) remains a formidable challenge. Existing higher-order enabled ILP systems show improved accuracy and learning performance, though remain hampered by the limitations of the underlying learning mechanism. Experimental results show that our extension of the versatile Learning From Failures paradigm by higher-order definitions significantly improves learning performance without the burdensome human guidance required by existing systems. Our theoretical framework captures a class of higher-order definitions preserving soundness of existing subsumption-based pruning methods.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 29 Dec 2021.

Builds on

9 of 43 references.

References · 9

  1. 2017Learning Explanatory Rules from Noisy Datacited by 211
  2. 2017Multi-shot ASP solving with clingocited by 115
  3. 2020Learning programs by learning from failurescited by 73
  4. 2021Inductive logic programming at 30cited by 47
  5. 2019Logical reduction of metarulescited by 19
  6. 2018Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learningcited by 12
  7. 2019Learning higher-order logic programscited by 11
  8. 2021Predicate Invention by Learning From Failurescited by 9
  9. 2019Forgetting to learn logic programscited by 8

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 4; 2024: 0; 2025: 5.

14 papers.

Sort:

  1. 2023Learning MDL logic programs from noisy datacited by 15
  2. 2022Learning logic programs by combining programscited by 7
  3. 2023Anti-unification and Generalization: A Surveycited by 7
  4. 2023Generalisation Through Negation and Predicate Inventioncited by 5
  5. 2022Learning programs with magic valuescited by 2
  6. 2022Relational program synthesis with numerical reasoningcited by 2
  7. 2022Rule Learning by Modularitycited by 1
  8. 2023Learning logic programs by discovering higher-order abstractionscited by 1
  9. 2022Differentiable Inductive Logic Programming in High-Dimensional Spacecited by 0
  10. 2025Efficient rule induction by ignoring pointless rulescited by 0
  11. 2025Honey, I shrunk the hypothesis space (through logical preprocessing)cited by 0
  12. 2025Symmetry breaking for inductive logic programmingcited by 0
2 more
  1. 2025Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategycited by 0
  2. 2025Satisfiability Modulo Theory Meets Inductive Logic Programmingcited by 0