Proximal algorithms are useful for obtaining solutions to difficult optimization problems, especially those involving nonsmooth or composite objective functions. A proximal algorithm is one whose ...

As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed ...

Machine-learning algorithms find and apply patterns in data. And they pretty much run the world. Machine-learning algorithms are responsible for the vast majority of the artificial intelligence ...

At the conceptual intersection of machine learning and government data collection lie Automated Suspicion Algorithms, or ASAs, which are created by applying machine learning methods to collections of ...

Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality ...

Want to understand how machine learning impacts search? Learn how Google uses machine learning models and algorithms in search.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

From Apple to Google to Toyota, companies across the world are pouring resources into developing AI systems with machine learning. This comprehensive guide explains what machine learning really means.