Marek Petrik of IBM’s T.J. Watson Research Center will be speaking in the Machine Learning and Friends lunch this Thursday, 12 March at 12:00pm in CS 150. His talk is titled “Better Solutions From Inaccurate Models” (abstract below).

Better Solutions From Inaccurate Models

It is very important in many application domains to compute good solutions from inaccurate models. Models in machine learning are inaccurate because they both simplify reality and are based on imperfect data. Robust optimization has emerged as a very powerful methodology for reducing solution sensitivity to model errors. In the first part of the talk, I will describe how robust optimization can mitigate data limitations in planning a large-scale disaster recovery operation. In the second part of the talk, I will discuss a novel use of robustness to substantially reducing error due to model simplification in reinforcement learning and large-scale regression.