No Regrets in Game Theory and Machine Learning

Spring 2013
Instructors: Michael Kearns and Aaron Roth
Time: Friday 12:00-3:00
Room: Levine 512
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Overview: In this class we will study applications of the remarkable multiplicative weights update method and related techniques to computer science. Application areas include game theory and mechanism design, learning theory, complexity theory, combinatorial optimization, differential privacy, and more. The class will be run in a seminar style. The instructors will give the first two lectures, and after that, students will choose papers to read and present. The entire class is expected to -read- the paper being presented that week, but the presenter will have the responsibility of teaching the material. We will have occasional guest lectures from experts.

Prerequisites: This will be a mathematically rigorous theory course intended for graduate students. There are no formal prerequisistes other than mathematical maturity. You should be comfortable reading and presenting formal mathematical material at a rigorous level.

Goals and Grading: Grading will be on the basis of your presentations as well as participation during the presentations of others.

Textbook: There will be no textbook, but we will be loosely following the survey paper of Arora, Hazan, and Kale. (AHK) Other useful references will be Chapter 4 of Algorithmic Game Theory (BM) by Blum and Mansour,  and the textbook "Boosting" (SF), by Schapire and Freund.

Office Hours: By appointment

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  • No Regrets Seminar Schedule