Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions.

In the United States, health insurance is a critical component of individuals' ability to access health care.

A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.

Autoregressive and Masked Transformers are incredibly effective as generative models and classifiers.

Tabular data that is organized in bi-dimensional matrices are widespread in webpages, documents, and databases.