A Pattern Language for Machine Learning Tasks

TMLR Paper4822 Authors

Abstract:

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.

Submission Length: Long submission (more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=G7TaH3yVjn

Changes Since Last Submission: Fixed font issues which lead to a desk reject.

Assigned Action Editor: ~Stefano_Teso1

Submission Number: 4822