8 citations · 17 across the 13 of their papers we have counts for
16 papers
Decision-Tree Surrogates for Weak Counterfactual Explanations in Integer Optimization
Marc Goerigk, Jannis Kurtz, Sebastian Merten
The concept of counterfactual explanations (CEs) has recently gained increasing attention for deriving explanations of mathematical optimization problems. For a given problem, a co…
The complexity landscape of robust (integer) linear programming
Michael Poss, Jannis Kurtz, Marc Goerigk +1
We study the computational complexity of the decision versions of three classic robust optimization problems: static robust optimization, two-stage (adjustable) robust optimization…
Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions
Jannis Kurtz, Bart P. G. van Parys
Optimization problems routinely depend on uncertain parameters that must be predicted before a decision is made. Classical robust and regret formulations are designed to handle err…
A Frank-Wolfe Algorithm for Oracle-based Robust Optimization
Mathieu Besançon, Jannis Kurtz
We tackle robust optimization problems under objective uncertainty in the oracle model, i.e., when the deterministic problem is solved by an oracle. The oracle-based setup is favor…
Bounding the Optimal Number of Policies for Robust K-Adaptability
Jannis Kurtz
In the realm of robust optimization the k-adaptability approach is one promising method to derive approximate solutions for two-stage robust optimization problems. Instead of allow…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…
Ensemble Methods for Robust Support Vector Machines using Integer Programming
Jannis Kurtz
In this work we study binary classification problems where we assume that our training data is subject to uncertainty, i.e. the precise data points are not known. To tackle this is…
Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization
Marc Goerigk, Jannis Kurtz
We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. We propose a machine-learning-based heuristic to determine starting…
Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
Jannis Kurtz, Bubacarr Bah
Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and comput…
Approximation Algorithms for Min-max-min Robust Optimization and K-Adaptability under Objective Uncertainty
Jannis Kurtz
In this work we investigate the min-max-min robust optimization problem and the k-adaptability robust optimization problem for binary problems with uncertain costs. The idea of the…
Data-Driven Robust Optimization using Unsupervised Deep Learning
Marc Goerigk, Jannis Kurtz
Robust optimization has been established as a leading methodology to approach decision problems under uncertainty. To derive a robust optimization model, a central ingredient is to…
An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
Bubacarr Bah, Jannis Kurtz
We study deep neural networks with binary activation functions (BDNN), i.e. the activation function only has two states. We show that the BDNN can be reformulated as a mixed-intege…
Min-Max-Min Robustness for Combinatorial Problems with Discrete Budgeted Uncertainty
Marc Goerigk, Jannis Kurtz, Michael Poss
We consider robust combinatorial optimization problems with cost uncertainty where the decision maker can prepare K solutions beforehand and chooses the best of them once the true…
Oracle-Based Algorithms for Binary Two-Stage Robust Optimization
Nicolas Kämmerling, Jannis Kurtz
In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-sta…
Discrete Optimization Methods for Group Model Selection in Compressed Sensing
Bubacarr Bah, Jannis Kurtz, Oliver Schaudt
In this article we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear…
Faster Algorithms for Min-max-min Robustness for Combinatorial Problems with Budgeted Uncertainty
André Chassein, Marc Goerigk, Jannis Kurtz +1
We consider robust combinatorial optimization problems where the decision maker can react to a scenario by choosing from a finite set of solutions. This approach is appropriate…