Teaching is inextricably linked to learning, and there are many studies on the complexity of
teaching as well as learning in computational learning theory. In this paper, we study the …

In a typical algorithmic learning model, a learner has to identify a target object from partial
information. Conversely, in a teaching model a teacher has to give information that allows …

Learning theory focuses almost entirely on the learner and its efficient realization, but
neglects other parts of the learning process, most importantly the teacher, which is merely …

Some recent work in computational learning theory has focused on the complexity of
teaching by examples. In this paper we study two combinatorial measures expressing the …

The present paper surveys recent developments in algorithmic teaching. First, the traditional
teaching dimension model is recalled. Starting from the observation that the teaching …

In machine teaching, a concept is represented by (and inferred from) a small number of
labeled examples. Various teaching models in the literature cast the interaction between …

Goldman and Kearns [GK91] recently introduced a notion of the teaching dimension of a
concept class. The teaching dimension is intended to capture the combinatorial difficulty of …

Abstract The Recursive Teaching Dimension (RTD) of a concept class\mathcalC is a
complexity parameter referring to the worst-case number of labelled examples needed to …

In computational learning theory, concepts are subsets of a set of instances and a concept
class is a set of concepts. In many computational learning models, learning algorithms have …

Linear sets are the building blocks of semilinear sets, which are in turn closely connected to
automata theory and formal languages. Prior work has investigated the learnability of linear …