relevanttien

relevanttien is a theoretical construct used in information science and data analysis to describe the minimal set of information from a data source that remains useful for a specific task. It is intended to separate task-relevant content from irrelevant or redundant data.

Unlike general measures of informativeness, relevanttien is defined with respect to a defined objective and a chosen modeling framework. It is related to concepts such as mutual information, sufficient statistics, and feature importance but is not synonymous with any of them. Different researchers propose different formalizations, reflecting varying priorities such as predictive accuracy, interpretability, or data efficiency.

The term arose in informal scholarly discussions and in some theoretical papers in the late 2010s and early 2020s. It has not achieved broad consensus or formal standardization, and many practitioners have not adopted it in favor of established tools.

One formal variant treats relevanttien as the information projection that preserves predictive performance across models within a class. Another variant frames it like an information bottleneck objective focused on retaining signals most relevant to the target variable while discarding noise. Both interpretations emphasize the idea of concentrating on information that directly supports the task.

Possible applications include guiding feature selection, data minimization for streaming or edge deployment, and informing data collection strategies. Limitations include dependence on the chosen objective and modeling assumptions, computational complexity, and sensitivity to distribution shifts. See also: mutual information, sufficiency, information bottleneck, feature selection.