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 ...

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

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 ...

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 is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian Learning, Decision Trees, Genetic Algorithms, Neural Networks.

It doesn’t take much to make machine-learning algorithms go awry The rise of large-language models could make the problem worse ...