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Found 6 papers, 3 papers with code
Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard.
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions.
Metrizing Fairness
This means that the distributions of the predictions within the two groups should be close with respect to the Kolmogorov distance, and fairness is achieved by penalizing the dissimilarity of these two distributions in the objective function of the learning problem.
A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity.
A Statistical Test for Probabilistic Fairness
Leveraging the geometry of the feature space, the test statistic quantifies the distance of the empirical distribution supported on the test samples to the manifold of distributions that render a pre-trained classifier fair.
Unifying Distributionally Robust Optimization via Optimal Transport Theory
In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods.
