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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.02424 |
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| _version_ | 1866916718565130240 |
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| author | Rodatz, Benjamin Fan, Ian Laakkonen, Tuomas Ortega, Neil John Hoffmann, Thomas Wang-Mascianica, Vincent |
| author_facet | Rodatz, Benjamin Fan, Ian Laakkonen, Tuomas Ortega, Neil John Hoffmann, Thomas Wang-Mascianica, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Pattern Language for Machine Learning Tasks Rodatz, Benjamin Fan, Ian Laakkonen, Tuomas Ortega, Neil John Hoffmann, Thomas Wang-Mascianica, Vincent Machine Learning Category Theory 18M30, 68T01 I.2.6 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. |
| title | A Pattern Language for Machine Learning Tasks |
| topic | Machine Learning Category Theory 18M30, 68T01 I.2.6 |
| url | https://arxiv.org/abs/2407.02424 |