Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of
interpretability, and a need for large amounts of training data. We survey recent work in …

We describe an inductive logic programming (ILP) approach called learning from failures. In
this approach, an ILP system (the learner) decomposes the learning problem into three …

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to
induce a hypothesis (a logic program) that generalises given training examples and …

Despite recent advances in modern machine learning algorithms, the opaqueness of their
underlying mechanisms continues to be an obstacle in adoption. To instill confidence and …

We propose a new approach to synthesize Datalog programs from input-output
specifications. Our approach leverages query provenance to scale the counterexample …

Modifications to the data representation of an abstract data type (ADT) can require
significant semantic refactoring of the code. Motivated by this observation, this paper …

Many recent works have proposed methods to train classifiers with local robustness
properties, which can provably eliminate classes of evasion attacks for most inputs, but not …

The problem of learning logical rules from examples arises in diverse fields, including
program synthesis, logic programming, and machine learning. Existing approaches either …

Datalog has witnessed promising applications in a variety of domains. We propose a
programming-by-example system, ALPS, to synthesize Datalog programs from input-output …