Combinatorial Modelling and Learning with Prediction Markets
This work addresses a foundational gap in machine learning by providing a unified approach to model combination, which could benefit researchers and practitioners seeking to create new models.
The paper tackles the problem of understanding and generalizing combinatorial models in machine learning by proposing prediction markets, particularly Storkey's Machine Learning Markets, as a generic framework that can implement various popular combinatorial structures under specific conditions.
Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's \emph{Machine Learning Markets} provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.