# Frauke Liers: (Data-Driven) Distributional Robustness: How to 'learn' relevant uncertainties together with robust decisions?

Abstract: In many applications, determining optimized solutions that are hedged

against uncertainties is mandatory. Classical stochastic optimization

approaches, however, may be prone to the disadvantage that the

underlying probability distributions are unknown or uncertain

themselves. On the other hand, standard robust optimization may lead

to conservative results as it generates solutions that are feasible

regardless of how uncertainties manifest themselves within predefined

uncertainty sets. Distributional robustness (DRO) lies at the

interface of robust and stochastic optimization as it robustly

protects against uncertain distributions. DRO currently receives

increased attention and is considered as an alternative that can lead

to less conservative but still uncertainty-protected solutions. In

this talk, we will review some approaches in the area of

distributional robustness. We will explain some recent developments

that use scenario observations to learn more about the uncertain

distributions over time, together with best possible protected

solutions. The goal is to incorporate new information when it arrives

and to improve our solution without resolving the entire robust

counterpart. We also present results for DRO applications in energy

and in the design of nanoparticles.

The first part of the talk is joint with K. Aigner, A. Barmann, K. Braun, F. Liers, S. Pokutta, O. Schneider, K. Sharma, and S. Tschuppik.

**Time: **
Fri 2023-05-05 11.00 - 12.00

**Location: **
3721

**Video link: **
Zoom

**Language: **
English

**Participating: **
Frauke Liers (FAU Erlangen)