5 papers
Scenario Reduction for Distributionally Robust Optimization
Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers +2
Stochastic and (distributionally) robust optimization problems often become computationally challenging as the number of scenarios or data points increases. Scenario reduction is t…
Norm-induced Cuts: Outer Approximation for Lipschitzian Constraint Functions
Adrian Göß, Alexander Martin, Sebastian Pokutta +1
In this paper, we consider a finite-dimensional optimization problem minimizing a continuous objective on a compact domain subject to a multi-dimensional constraint function. For t…
Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe
Kartikey Sharma, Deborah Hendrych, Mathieu Besançon +1
We tackle the network design problem for centralized traffic assignment, which can be cast as a mixed-integer convex optimization (MICO) problem. For this task, we propose differen…
Data-driven Distributionally Robust Optimization over Time
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun +5
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters…
Interpretability Guarantees with Merlin-Arthur Classifiers
Stephan Wäldchen, Kartikey Sharma, Berkant Turan +2
We propose an interactive multi-agent classifier that provides provable interpretability guarantees even for complex agents such as neural networks. These guarantees consist of low…