Invited Talk: Learning from Rational Behavior

The ability to learn from user interactions can give systems access to unprecedented amounts of knowledge. This is evident in search engines, recommender systems, and electronic commerce, and it can be the key to solving other knowledge intensive tasks. However, extracting the knowledge conveyed by user interactions is less straightforward than standard machine learning, since it requires learning systems that explicitly account for human decision making, human motivation, and human abilities. In this talk, I argue that the design space of such interactive learning systems encompasses not only the machine learning algorithm itself, but also the design of the interaction under an appropriate model of user behavior. To this effect, the talk explores how integrating microeconomic models of human behavior into the learning process leads to new interaction models and their associated learning algorithms, leading to systems that have provable guarantees and that perform robustly in practice.