As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …

Many visual recognition models are evaluated only on their classification accuracy, a metric
for which they obtain strong performance. In this paper, we investigate whether computer …

Spurious correlations that degrade model generalization or lead the model to be right for the
wrong reasons are one of the main robustness concerns for real-world deployments …

Deep learning has revolutionized the field of artificial intelligence. Based on the statistical
correlations uncovered by deep learning-based methods, computer vision tasks, such as …

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting.
While visual prompts offer a lightweight method of input-space adaptation for large-scale …

The problem of bias in facial affect recognition tools can lead to severe consequences and
issues. It has been posited that causality is able to address the gaps induced by the …

Abstract Machine learning models face generalization challenges when exposed to out-of-
distribution (OOD) samples with unforeseen distribution shifts. Recent research reveals that …