Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

Socially assistive robots (SAR) have shown great potential to augment the social and
educational development of children with autism spectrum disorders (ASD). As SAR …

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 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 …

In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the
teacher provides examples to the learner iteratively such that the learner can achieve fast …

We study black-box reward poisoning attacks against reinforcement learning (RL), in which
an adversary aims to manipulate the rewards to mislead a sequence of RL agents with …

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 …

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 …

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 …

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching
(MT) algorithms for online personalized education on home devices. This direction becomes …