Stefanie Jegelka, Massachusetts Institute of Technology

Abstract

When critical decisions and predictions rely on observed data, robustness is an important consideration in learning and optimization. Robust formulations, however, can lead to more challenging, e.g., nonconvex, optimization problems. This talk will summarize some recent ideas at the intersection of robust optimization and submodular optimization. In particular, submodular optimization can help robust optimization, and vice versa: first, we show how ideas from discrete optimization lead to solving a nonconvex robust allocation or bidding problem; second, we develop algorithms for stochastic submodular optimization via robust submodular optimization. In both cases, the submodularity property offers the basis for a rich interplay of discrete and continuous optimization.

This talk is based on joint work with Matthew Staib and Bryan Wilder.