activity
20192025
most citedBeneficial and Harmful Explanatory Machine Learning

5 citations · 7 across the 13 of their papers we have counts for

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9 papers · 1 filter

cs.LG2025

An Empirical Comparison of Cost Functions in Inductive Logic Programming

Céline Hocquette, Andrew Cropper

Recent inductive logic programming (ILP) approaches learn optimal hypotheses. An optimal hypothesis minimises a given cost function on the training data. There are many cost functi…

cs.LG2024

Learning logic programs by finding minimal unsatisfiable subprograms

Andrew Cropper, Céline Hocquette

The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identi…

cs.LG2024

Learning big logical rules by joining small rules

Céline Hocquette, Andreas Niskanen, Rolf Morel +2

A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We impleme…

cs.LG2023

Learning MDL logic programs from noisy data

Céline Hocquette, Andreas Niskanen, Matti Järvisalo +1

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…

cs.LG2023

Learning logic programs by discovering higher-order abstractions

Céline Hocquette, Sebastijan Dumančić, Andrew Cropper

We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold. We impleme…

cs.LG2022

Relational program synthesis with numerical reasoning

Céline Hocquette, Andrew Cropper

Program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values over multiple examples, such as interva…

cs.LG2022

Learning programs with magic values

Céline Hocquette, Andrew Cropper

A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice. Learning programs with magic values…

cs.LG2022★ 2 cited

Learning logic programs by combining programs

Andrew Cropper, Céline Hocquette

The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples. Inducing programs with many rules and literals is…

cs.LG2022

Learning logic programs by discovering where not to search

Andrew Cropper, Céline Hocquette

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce a…