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 …

I draw the reader's attention to machine teaching, the problem of finding an optimal training
set given a machine learning algorithm and a target model. In addition to generating …

In this paper, we consider the problem of machine teaching, the inverse problem of machine
learning. Different from traditional machine teaching which views the learners as batch …

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing
for policy improvement and generalization. However, despite much recent interest in IRL …

In this article, we propose a communicative learning (CL) formalism that unifies existing
machine learning paradigms, such as passive learning, active learning, algorithmic …

We study a dynamic model of Bayesian persuasion in sequential decision-making settings.
An informed principal observes an external parameter of the world and advises an …

In this paper, we make an important step towards the black-box machine teaching by
considering the cross-space machine teaching, where the teacher and the learner use …

Teaching dimension is a learning theoretic quantity that speciés the minimum training set
size to teach a target model to a learner. Previous studies on teaching dimension focused on …

Modern machine learning methods are increasingly powerful and opaque. This opaqueness
is a concern across a variety of domains in which algorithms are making important decisions …

In real-world applications of education, an effective teacher adaptively chooses the next
example to teach based on the learner's current state. However, most existing work in …