Bartolomeo Stellato @stella.to · Nov 29 1 / 2
In Mean Robust Optimization, we define an uncertainty set around the cluster centroids with weights defined by the amount of samples in each cluster. 🧵 (4/7)
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By varying the number of clusters K, our method bridges Robust and Distributionally Robust optimization! We also derive theoretical bounds on 1) how to adjust the Wasserstein ball radius to compensate for clustering, and 2) how to exactly quantify the effect of clustering 🧵 (5/7)