The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

Abstract Machine learning brings the hope of finding new biomarkers extracted from cohorts
with rich biomedical measurements. A good biomarker is one that gives reliable detection of …

As the use of machine learning in high impact domains becomes widespread, the
importance of evaluating safety has increased. An important aspect of this is evaluating how …

We study distributionally robust optimization (DRO) with Sinkhorn distance--a variant of
Wasserstein distance based on entropic regularization. We derive convex programming …

In this paper, we consider learning scenarios where the learned model is evaluated under
an unknown test distribution which potentially differs from the training distribution (ie …

We review distributionally robust optimization (DRO), a principled approach for constructing
statistical estimators that hedge against the impact of deviations in the expected loss …

The performance of ML models degrades when the training population is different from that
seen under operation. Towards assessing distributional robustness, we study the worst-case …

Moment restrictions and their conditional counterparts emerge in many areas of machine
learning and statistics ranging from causal inference to reinforcement learning. Estimators …

Several recent works have studied the societal effects of AI; these include issues such as
fairness, robustness, and safety. In many of these objectives, a learner seeks to minimize its …

Wasserstein distributionally robust estimators have emerged as powerful models for
prediction and decision-making under uncertainty. These estimators provide attractive …