8 citations · 9 across the 3 of their papers we have counts for
8 papers
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…
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…
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…