20 citations · 37 across the 8 of their papers we have counts for
16 papers
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…
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Tobias Sutter, Andreas Krause, Daniel Kuhn
Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to train…
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man-Chung Yue, Jose Blanchet +2
Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additi…
A Statistical Test for Probabilistic Fairness
Bahar Taskesen, Jose Blanchet, Daniel Kuhn +1
Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algo…
A Distributionally Robust Approach to Fair Classification
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn +1
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnic…
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…