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.

翻译：尽管机器学习任务对输入数据的质量高度敏感，但相关数据集往往难以被企业获取，尤其是当数据由不同所有者私有时。例如，若这些所有者在下游市场中互为竞争者，他们可能不愿共享信息。针对回归任务的监督学习，我们设计了一个回归市场，为数据共享提供货币激励。我们的机制采用贝叶斯框架，使我们能够考虑更广泛的回归任务类别。我们对市场特性进行了深入探讨，并表明文献中的类似方案使市场参与者面临巨大的财务风险，而我们的设置能够缓解此类风险。