This workshop focuses on “machine teaching”, the inverse problem of machine learning, in which the goal is to find an optimal training set given a machine learning algorithm and a target model. The study of machine teaching began in the early 1990s, primarily coming out of computational learning theory. Recently, there has been a surge of interest in machine teaching as several different communities within machine learning have found connections to this problem; these connections have included the following:

* machine teaching has close connections to newly introduced models of interaction in machine learning community, such as curriculum learning, self-paced learning, and knowledge distillation. [Hinton et al. 2015; Bengio et al. 2009]

* there are strong theoretical connections between the Teaching-dimension (the sample complexity of teaching) and the VC-dimension (the sample complexity of learning from randomly chosen examples). [Doliwa et al. 2014]

* machine teaching problem formulation has been recently studied in the context of diverse applications including personalized educational systems, cyber-security problems, robotics, program synthesis, human-in-the-loop systems, and crowdsourcing. [Jha et al. 2016; Zhu 2015; Mei & Zhu 2015; Ba & Caruana 2014; Patil et al. 2014; Singla et al. 2014; Cakmak & Thomaz 2014]

In this workshop, we draw attention to machine teaching by emphasizing how the area of machine teaching interacts with emerging research trends and application domains relevant to the NIPS community. The goal of this workshop is to foster these ideas by bringing together researchers with expertise/interest in the inter-related areas of machine teaching, interactive machine learning, robotics, cyber-security problems, generative adversarial networks, educational technologies, and cognitive science.

Topics of interests in the workshop include (but are not limited to):

* Theoretical foundations of machine teaching:
* using tools from information theory to develop better mathematical models of teaching;
* characterizing the complexity of teaching when a teacher has limited power, or incomplete knowledge of student’s model, or a mismatch in feature representations;
* algorithms for adaptive teaching by interactively inferring the learner’s state;
* new notions of Teaching-dimension for generic teaching settings.

* Connections to machine learning models:
* the information complexity of teaching and query complexity;
* machine teaching vs. curriculum learning and other models of interactive machine learning;
* teaching reinforcement learning agents.

Applications of machine teaching to adversarial attacks, including cyber-security problems, generative adversarial networks, attacks on machine learning algorithms, etc.

* Applications of machine teaching to educational technologies:
* using the machine teaching formulation to enable more rigorous and generalizable approaches for developing intelligent tutoring systems;
* behavioral experiments to identify good cognitive models of human learning processes.

* Novel applications for machine teaching such as program synthesis, human-robot interactions, social robotics, etc.