5 citations · 7 across the 13 of their papers we have counts for
13 papers
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…
Relational decomposition for program synthesis
Céline Hocquette, Andrew Cropper
We introduce a relational approach to program synthesis. The key idea is to decompose synthesis tasks into simpler relational synthesis subtasks. Specifically, our representation d…
Can humans teach machines to code?
Céline Hocquette, Johannes Langer, Andrew Cropper +1
The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide suf…
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…
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…
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…
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…
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…
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…
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…
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…
Beneficial and Harmful Explanatory Machine Learning
Lun Ai, Stephen H. Muggleton, Céline Hocquette +2
Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this con…
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
Céline Hocquette, Stephen H. Muggleton
World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, owing to tractabilit…