We formalise the essential data of objective functions as equality constraints on composites of learners. We call these constraints "tasks", and we investigate the idealised view that such tasks determine model behaviours. We develop a flowchart-like graphical mathematics for tasks that allows us to; (1) offer a unified perspective of approaches in machine learning across domains; (2) design and optimise desired behaviours model-agnostically; and (3) import insights from theoretical computer science into practical machine learning. As a proof-of-concept of the potential practical impact of our theoretical framework, we exhibit and implement a novel "manipulator" task that minimally edits input data to have a desired attribute. Our model-agnostic approach achieves this end-to-end, and without the need for custom architectures, adversarial training, random sampling, or interventions on the data, hence enabling capable, small-scale, and training-stable models.
View on arXiv@article{rodatz2025_2407.02424,
title={ A Pattern Language for Machine Learning Tasks },
author={ Benjamin Rodatz and Ian Fan and Tuomas Laakkonen and Neil John Ortega and Thomas Hoffmann and Vincent Wang-Mascianica },
journal={arXiv preprint arXiv:2407.02424},
year={ 2025 }
}