Redundant objectives in multiobjective optimization

This work examines three different concepts of objective redundancy in multiobjective optimization. Multiobjective optimization is known to suffer from the curse of dimensionality and we aim at reducing the number of objective functions which need to be considered. In this context, a set of objectives is called redundant if the sets of weakly efficient, efficient, … Read more

Projection Robust Wasserstein Barycenters

Collecting and aggregating information from several probability measures or histograms is a fundamental task in machine learning. One of the popular solution methods for this task is to compute the barycenter of the probability measures under the Wasserstein metric. However, approximating the Wasserstein barycenter is numerically challenging because of the curse of dimensionality. This paper … Read more