Methods for Adaptive Seeding and their Applications

Tuesday, November 19, 2013 - 4:15pm to 5:15pm
3:45pm in 32-G449 (Patil/Kiva)
Yaron Singer, Harvard University


Adaptive seeding is a two-stage stochastic optimization framework recently developed for information dissemination in social networks. The goal is to optimize a combinatorial function by making an initial decision that affects the realizations selected by nature. Beyond information dissemination in networks other interesting applications are in machine learning and operations research. In this talk we will discuss several optimization techniques for adaptive seeding as well as results in social network analysis that motivate this approach.