220 citations · 724 across the 45 of their papers we have counts for
6 papers · 1 filter
Wasserstein Distributionally Robust Kalman Filtering
Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn +1
We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal es…
Data-Driven Chance Constrained Programs over Wasserstein Balls
Zhi Chen, Daniel Kuhn, Wolfram Wiesemann
We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constr…
Resolution of the Oberwolfach problem
Stefan Glock, Felix Joos, Jaehoon Kim +2
The Oberwolfach problem, posed by Ringel in 1967, asks for a decomposition of into edge-disjoint copies of a given -factor. We show that this can be achieved for all…
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a -dimensional Gaussian ran…
Distributionally robust optimization with polynomial densities: theory, models and algorithms
Etienne de Klerk, Daniel Kuhn, Krzysztof Postek
In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the…
On a conjecture of Erdős on locally sparse Steiner triple systems
Stefan Glock, Daniela Kühn, Allan Lo +1
A famous theorem of Kirkman says that there exists a Steiner triple system of order if and only if . In 1973, Erdős conjectured that one can find so-called…