A Pattern Language for Machine Learning Tasks

2024-07-02

arXiv.org

AI Summary

Existing machine learning frameworks suffer from insufficient formalization of objective functions and lack a unified, cross-domain behavioral design paradigm. Method: We propose an equation-constrained compositional function modeling approach for learners, constructing task graphs and compositional semantic graphs to enable model-agnostic behavioral specification and optimization. We introduce a novel task-oriented pattern language framework and the “manipulator” task paradigm, supporting end-to-end, architecture-agnostic, and adversarial-training-free minimal editing of data attributes. Contribution/Results: Theoretically, our work integrates formal methods and theoretical computer science principles. Empirically, we demonstrate precise, controllable, and interpretable behavioral editing on small-scale models under stable training—without stochastic sampling or data intervention—yielding significant improvements in deployment efficiency and formal verifiability.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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; (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.

Problem

• Formalizing objective functions as equality constraints on learners
• Developing a graphical mathematics for unified ML task design
• Creating model-agnostic tasks for stable small-scale ML models

Innovation

• Formalize tasks as equality constraints on learners
• Develop flowchart-like graphical mathematics for tasks
• Implement model-agnostic manipulator task without custom architectures