A Pattern Language for Machine Learning Tasks

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; offer a unified perspective of approaches in machine learning across domains; design and optimise desired behaviours model-agnostically; and import insights from theoretical computer science into practical machine learning. As preliminary experimental validation of our theoretical framework, we exhibit and implement a novel ``manipulation'' 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.

Authors

• Benjamin Rodatz
• Ian Fan
• Tuomas Laakkonen
• Neil John Ortega
• Thomas Hoffmann
• Vincent Wang

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