English

Bayesian Regression Markets

Machine Learning 2024-07-02 v3

Abstract

Although machine learning tasks are highly sensitive to the quality of input data, relevant datasets can often be challenging for firms to acquire, especially when held privately by a variety of owners. For instance, if these owners are competitors in a downstream market, they may be reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in literature expose the market agents to sizeable financial risks, which can be mitigated in our setup.

Keywords

Cite

@article{arxiv.2310.14992,
  title  = {Bayesian Regression Markets},
  author = {Thomas Falconer and Jalal Kazempour and Pierre Pinson},
  journal= {arXiv preprint arXiv:2310.14992},
  year   = {2024}
}

Comments

35 pages, 11 figures, 3 tables. Published in Journal of Machine Learning Research (2024)

R2 v1 2026-06-28T12:59:02.961Z