Distribution shifts occur when the test distribution differs from the training distribution, and
can considerably degrade performance of machine learning models deployed in the real …

Evaluating outputs of large language models (LLMs) is challenging, requiring making—and
making sense of—many responses. Yet tools that go beyond basic prompting tend to require …

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …

Infection science is a discipline of healthcare which includes clinical microbiology, public
health microbiology, mechanisms of microbial disease, and antimicrobial countermeasures …

Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

Prediction models can perform poorly when deployed to target distributions different from the
training distribution. To understand these operational failure modes, we develop a method …

Insider threats on information security can become a burden for organizations. However,
outsider attacks have received more attention compared to insider attacks. Many …

Machine learning (ML) has become an indispensable tool to predict absorption, distribution,
metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms …

Uncertainty is a key feature of any machine learning model and is particularly important in
neural networks, which tend to be overconfident. This overconfidence is worrying under …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …