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 …

We study the problem of inverse reinforcement learning (IRL) with the added twist that the
learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic …

We study the problem of computer-assisted teaching with explanations. Conventional
approaches for machine teaching typically only provide feedback at the instance level eg …

We study the problem of teaching multiple learners simultaneously in the nonparametric
iterative teaching setting, where the teacher iteratively provides examples to the learner for …

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 …

With the advent and growing use of crowdsourcing labor markets for a variety of
applications, optimizing the quality of results produced is of prime importance. The quality of …

Learning near-optimal behaviour from an expert's demonstrations typically relies on the
assumption that the learner knows the features that the true reward function depends on. In …

In this paper, we consider the problem of iterative machine teaching, where a teacher
provides examples sequentially based on the current iterative learner. In contrast to previous …

Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by
observing demonstrations from a (near-) optimal policy. The typical assumption is that the …

Algorithmic machine teaching studies the interaction between a teacher and a learner where
the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest to …