Machine Learning Markets

Amos Storkey
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:716-724, 2011.

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. 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 implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-storkey11a, title = {Machine Learning Markets}, author = {Storkey, Amos}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {716--724}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/storkey11a/storkey11a.pdf}, url = {https://proceedings.mlr.press/v15/storkey11a.html}, 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. 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 implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions.} }
Endnote
%0 Conference Paper %T Machine Learning Markets %A Amos Storkey %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-storkey11a %I PMLR %P 716--724 %U https://proceedings.mlr.press/v15/storkey11a.html %V 15 %X 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. 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 implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions.
RIS
TY - CPAPER TI - Machine Learning Markets AU - Amos Storkey BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-storkey11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 716 EP - 724 L1 - http://proceedings.mlr.press/v15/storkey11a/storkey11a.pdf UR - https://proceedings.mlr.press/v15/storkey11a.html AB - 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. 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 implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. ER -
APA
Storkey, A.. (2011). Machine Learning Markets. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:716-724 Available from https://proceedings.mlr.press/v15/storkey11a.html.

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