Toward a `Standard Model' of Machine Learning
This addresses the problem of disparate, narrowly focused methods hindering standardized and reusable ML development for researchers and practitioners, though it is incremental as it builds on existing paradigms.
The paper tackles the lack of standardization in machine learning by proposing a 'standard equation' formalism that unifies various ML algorithms across paradigms like supervised, unsupervised, and reinforcement learning, enabling mechanical design of new approaches and aiming for panoramic learning from all experience types.
Machine learning (ML) is about computational methods that enable machines to learn concepts from experience. In handling a wide variety of experience ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly focused methods also make standardized, composable, and reusable development of ML approaches difficult, and preclude the opportunity to build AI agents that panoramically learn from all types of experience. This article presents a standardized ML formalism, in particular a `standard equation' of the learning objective, that offers a unifying understanding of many important ML algorithms in the supervised, unsupervised, knowledge-constrained, reinforcement, adversarial, and online learning paradigms, respectively -- those diverse algorithms are encompassed as special cases due to different choices of modeling components. The framework also provides guidance for mechanical design of new ML approaches and serves as a promising vehicle toward panoramic machine learning with all experience.