Machine Learning Markets

Abstract

Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is shown that such markets can implement model combination methods used in machine learning, such as product of expert and mixture of expert approaches as equilibrium pricing models, by varying agent utility functions. They can also implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. Conversely, the market mechanisms implement inference in the relevant probabilistic models. This means that market mechanism can be utilized for implementing parallelized model building and inference for probabilistic modelling.

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics 2011

References in corpus (4)

• Probabilistic Latent Semantic Analysis
• An Empirical Comparison of Algorithms for Aggregating Expert Predictions
• Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market
• Statistical properties of agent-based models in markets with continuous double auction mechanism

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• Isoelastic Agents and Wealth Updates in Machine Learning Markets
• Cortical prediction markets
• The Artificial Regression Market
• Information Aggregation in Exponential Family Markets
• Towards Error Measures which Influence a Learners Inductive Bias to the Ground Truth
• Semantics, Representations and Grammars for Deep Learning
• Combinatorial Modelling and Learning with Prediction Markets
• Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report