In curriculum learning the order of concepts is determined by the teacher but not the
examples for each concept, while in machine teaching it is the examples that are chosen by …

Abstract Machine teaching under strong simplicity priors can teach any concept in universal
languages. Remarkably, recent experiments suggest that the teaching sets are shorter than …

Recent research in machine teaching has explored the instruction of any concept expressed
in a universal language. In this compositional context, new experimental results have shown …

We introduce and study minimax curriculum learning (MCL), a new method for adaptively
selecting a sequence of training subsets for a succession of stages in machine learning. The …

Curriculum Learning is motivated by human cognition, where teaching often involves
gradually exposing the learner to examples in a meaningful order, from easy to hard …

Recent automatic curriculum learning algorithms, and in particular Teacher-Student
algorithms, rely on the notion of learning progress, making the assumption that the good …

We consider a statistical version of curriculum learning (CL) in a parametric prediction
setting. The learner is required to estimate a target parameter vector, and can adaptively …

We present a theoretical study of machine teaching in the setting where the teacher must
use the same training set to teach multiple learners. This problem is a theoretical abstraction …

In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …

One widely-studied model of teaching calls for a teacher to provide the minimal set of
labeled examples that uniquely specifies a target concept. The assumption is that the …