The overarching goal of this work is to efficiently enable end-users to correctly anticipate a
robot's behavior in novel situations. And since a robot's behavior is often a direct result of its …

Under the guidance of a formal exemplar model of categorization, we conduct comparisons
of natural-science classification learning across four conditions in which the nature of 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 …

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 …

Given a sequential learning algorithm and a target model, sequential machine teaching
aims to find the shortest training sequence to drive the learning algorithm to the target …

How can we help a forgetful learner learn multiple concepts within a limited time frame?
While there have been extensive studies in designing optimal schedules for teaching a …

We consider the machine teaching problem in a classroom-like setting wherein the teacher
has to deliver the same examples to a diverse group of students. Their diversity stems from …

Because of their complex structures, many natural-science categories are difficult to learn.
Yet achieving accuracy in classification is crucial to scientific inference and reasoning. Thus …

Recently, spatial crowdsourcing has been drawing increasing attention with its great
potential in collecting geographical knowledge. The system throughput (number of assigned …

We develop the mathematical formulation for teaching generative models to a learner whose
learning processes and cognitive behaviors may be analytically intractable, but can be …