This series of talks is part of the Algorithms and Uncertainty Boot Camp. Videos for each talk area will be available through the links above.
Speaker: Kamesh Munagala, Duke University
This tutorial will present an overview of techniques from Approximation Algorithms as relevant to Stochastic Optimization problems. In these problems, we assume partial information about inputs in the form of distributions. Special emphasis will be placed on techniques based on linear programming and duality. The tutorial will assume no prior background in stochastic optimization.