20 citations · 37 across the 8 of their papers we have counts for
6 papers · 1 filter
Distributionally Robust Optimization with Markovian Data
Mengmeng Li, Tobias Sutter, Daniel Kuhn
We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples ge…
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn +1
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The propo…
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these…
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…
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…