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

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

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 …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

Neural networks are vulnerable to adversarial examples and researchers have proposed
many heuristic attack and defense mechanisms. We address this problem through the …

This paper deals with the problem of quantifying the impact of model misspecification when
computing general expected values of interest. The methodology that we propose is …

Generalization to out-of-distribution (OOD) data is one of the central problems in modern
machine learning. Recently, there is a surge of attempts to propose algorithms that mainly …

The goal of regression and classification methods in supervised learning is to minimize the
empirical risk, that is, the expectation of some loss function quantifying the prediction error …

Distributionally robust optimization (DRO) has attracted attention in machine learning due to
its connections to regularization, generalization, and robustness. Existing work has …

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