A benchmarking study in BMC Infectious Diseases shows that data balancing, feature selection, and hyperparameter tuning make machine learning models markedly more reliable for diagnosing hepatitis C ...

A first-of-its-kind systematic review of 190 studies finds that HASM, Euclidean-enhanced machine learning, and Bayesian ...

Susan C. Athey is an economist who has studied ways to analyze digital markets and policy by connecting economic theory, ...

A strong backtest may be evidence of a durable edge, or it may show how thoroughly an algorithm has adapted to historical noise. That distinction ...

Google moves federated learning into TEEs, giving Gboard externally verifiable central differential privacy and faster server-side training.

Stochastic gradient descent and Adam are optimization algorithms that update model parameters from estimated gradients, but ...

Spatial transcriptomics can reveal where thousands of genes are active across a tissue, creating molecular maps at single-cell resolution. But comparing two such maps is difficult: thin slices of ...

I often hear people say, "I want to study machine learning, but I don't know where to start." Some open a book on mathematical formulas only to close it immediately, while others burn out just trying ...

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a camera-based inspection system that uses AI and machine learning (ML) to check 3D printed parts while they are still being ...

Explore how an ai investing think tank blends machine learning and economic history to build resilient quantitative models and capture institutional alpha.

A loss function converts the difference between predictions and targets into a quantity that learning algorithms try to minimize. This guide explains the mechanism, trade-offs, evaluation, and ...

The current MA risk adjustment model has shortcomings, both in predictive accuracy and payment equity across the Medicare program, which could be mitigated using lessons from machine learning. MA ...