Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Advances in machine learning for health care have brought concerns about bias from the
research community; specifically, the introduction, perpetuation, or exacerbation of care …

Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the
robustness challenge under distribution shifts. However, the lack of consistent settings and …

To avoid failures on out-of-distribution data, recent works have sought to extract features that
have an invariant or stable relationship with the label across domains, discarding" spurious" …

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

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

Web-based content analysis tasks, such as labeling toxicity, misinformation, or spam often
rely on machine learning models to achieve cost and scale efficiencies. As these models …

We consider the problem of estimating the mean of a random variable Y subject to non-
ignorable missingness, ie, where the missingness mechanism depends on Y. We connect …

Deep neural networks (DNNs) have exhibited remarkable performance across various tasks,
yet their susceptibility to spurious correlations poses a significant challenge for out-of …

By default neural networks are not robust to changes in data distribution. This has been
demonstrated with simple image corruptions, such as blurring or adding noise, degrading …