Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …

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 …

Deep neural networks often rely on spurious correlations to make predictions, which hinders
generalization beyond training environments. For instance, models that associate cats with …

Different distribution shifts require different algorithmic and operational interventions.
Methodological research must be grounded by the specific shifts they address. Although …

In real-world classification tasks, each class often comprises multiple finer-grained"
subclasses." As the subclass labels are frequently unavailable, models trained using only …

We propose a tuning-free dynamic SGD step size formula, which we call Distance over
Gradients (DoG). The DoG step sizes depend on simple empirical quantities (distance from …

Spurious correlations pose a major challenge for robust machine learning. Models trained
with empirical risk minimization (ERM) may learn to rely on correlations between class …

Machine learning systems are known to be sensitive to spurious correlations between non-
essential features of the inputs (eg, background, texture, and secondary objects) and the …

Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …