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Computer Science > Machine Learning

arXiv:2310.14992 (cs)
[Submitted on 23 Oct 2023]

Title:Bayesian Regression Markets

Authors:Thomas Falconer, Jalal Kazempour, Pierre Pinson
Download a PDF of the paper titled Bayesian Regression Markets, by
Thomas Falconer and Jalal Kazempour and Pierre Pinson
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    Abstract: Machine learning tasks are vulnerable to the quality of
    data used as input. Yet, it is often challenging for firms to
    obtain adequate datasets, with them being naturally distributed
    amongst owners, that in practice, may be competitors in a
    downstream market and reluctant to share information. Focusing on
    supervised learning for regression tasks, we develop a \textit
    {regression market} to provide a monetary incentive for data
    sharing. Our proposed 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 current literature expose the
    market agents to sizeable financial risks, which can be mitigated
    in our probabilistic setting.

Comments: 46 pages, 11 figures, 2 tables
Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2310.14992 [cs.LG]
          (or arXiv:2310.14992v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2310.14992
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Thomas Falconer [view email]
[v1] Mon, 23 Oct 2023 14:45:51 UTC (338 KB)
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