In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks
on graphs, which become favorable comparing with other approaches. However, similar to …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

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 …

Abstract Distributionally Robust Optimization (DRO) serves as a robust alternative to
empirical risk minimization (ERM), which optimizes the worst-case distribution in an …

The ability to generalize under distributional shifts is essential to reliable machine learning,
while models optimized with empirical risk minimization usually fail on non-iid testing data …

Domain generalization (DG) aims to tackle the distribution shift between training domains
and unknown target domains. Generating new domains is one of the most effective …

Abstract Machine learning algorithms with empirical risk minimization are vulnerable under
distributional shifts due to the greedy adoption of all the correlations found in training data …

Machine learning algorithms with empirical risk minimization are vulnerable under
distributional shifts due to the greedy adoption of all the correlations found in training data …