Polynomial Regression

Polynomial regression, a method for modeling relationships between variables using polynomial functions, aims to accurately fit data and make predictions. Current research focuses on improving robustness to outliers, particularly in high-dimensional settings, and exploring its theoretical properties within the context of machine learning, including its relationship to kernel methods and neural networks. This renewed interest stems from its use in understanding complex phenomena like in-context learning in large language models and its potential for providing more interpretable alternatives to "black box" models in various applications, such as object location prediction and materials science.