Subpopulation shift widely exists in many real-world machine learning applications, referring
to the training and test distributions containing the same subpopulation groups but varying in …

Abstract Machine learning models often perform poorly under subpopulation shifts in the
data distribution. Developing methods that allow machine learning models to better …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

We develop a methodology for assessing the robustness of models to subpopulation shift---
specifically, their ability to generalize to novel data subpopulations that were not observed …

Importance weighting is a classic technique to handle distribution shifts. However, prior work
has presented strong empirical and theoretical evidence demonstrating that importance …

Robustness to distribution shifts is critical for deploying machine learning models in the real
world. Despite this necessity, there has been little work in defining the underlying …

Dealing with distribution shifts is one of the central challenges for modern machine learning.
One fundamental situation is the covariate shift, where the input distributions of data change …

Skewed class proportions in real-world datasets present a challenge for machine learning
algorithms, as they have a tendency to correctly categorize the majority class while …

Abstract Out-of-distribution (OOD) data poses serious challenges in deployed machine
learning models, so methods of predicting a model's performance on OOD data without …

Handling an imbalanced class problem is a challenging task in real-world applications. This
problem affects various prediction models that predict only the majority class and fail to …