Accepted Tutorials
TITLE: Interactive Machine Learning: From Classifiers to Robotics
ABSTRACT: In order to make virtual agents and physical robots solve real-world tasks, it often becomes necessary to learn not only from static datasets or simulated oracles, but directly from humans. Unfortunately, some of the assumptions underlying traditional statistical machine learning approaches become invalid when learning from data provided by slow, inaccurate, or inconsistent trainers. Furthermore, many additional considerations that are typically outside the purview of machine learning experts, such as user interface, become critical.
This tutorial will:
1) survey selected existing work in this exciting and growing field;
2) propose a framework to classify and understand different types of work in this area, as well as highlight important opportunities for additional work; and
3) cover a selection of practical considerations, such as participant recruitment and compensation, and useful toolkits or testbeds.
SPEAKER: Matthew E. Taylor
TUTORIAL WEB PAGE: Link