A Marketplace for Data: An Algorithmic Solution
A Marketplace for Data: An Algorithmic Solution
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DOI：
10.1145/3328526.3329589
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发表时间：
2018-05
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影响因子：
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通讯作者：
Anish Agarwal;M. Dahleh;Tuhin Sarkar
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作者：
Anish Agarwal;M. Dahleh;Tuhin Sarkar
在这项工作中，我们旨在设计一个数据市场。有效地购买和出售机器学习任务的培训数据的强大实时匹配机制。虽然数据和预培训模型的货币化是当今行业的重要重点，但并不存在市场机制来给培训数据的价格和将买方与卖方相匹配，同时仍在解决相关的（计算和其他）复杂性。创建这样的市场的挑战源于数据的本质作为资产：（i）它是可以自由复制的； （ii）由于与其他数据中的信号相关，其值本质上是组合的； （iii）预测任务和准确性的价值差异很大； （iv）培训数据的有用性很难在不先将其应用于预测任务的情况下验证先验。作为我们的主要贡献，我们：（i）为双面数据市场提出了一个数学模型，并正式定义了关键的相关挑战； （ii）为这种市场构建算法，以运行和分析它们如何应对定义的挑战。我们重点介绍了两个技术贡献：（i）具有自由复制品的合作游戏所需的新概念； （ii）一种真实的，零遗憾的机制，用于拍卖一类基于迈尔森的支付功能和乘法算法的组合商品。这些可能具有独立的兴趣。
In this work, we aim to design a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetization of data and pre-trained models is an essential focus of industry today, there does not exist a market mechanism to price training data and match buyers to sellers while still addressing the associated (computational and other) complexity. The challenge in creating such a market stems from the very nature of data as an asset: (i) it is freely replicable; (ii) its value is inherently combinatorial due to correlation with signal in other data; (iii) prediction tasks and the value of accuracy vary widely; (iv) usefulness of training data is difficult to verify a priori without first applying it to a prediction task. As our main contributions we: (i) propose a mathematical model for a two-sided data market and formally define the key associated challenges; (ii) construct algorithms for such a market to function and analyze how they meet the challenges defined. We highlight two technical contributions: (i) a new notion of "fairness" required for cooperative games with freely replicable goods; (ii) a truthful, zero regret mechanism to auction a class of combinatorial goods based on utilizing Myerson's payment function and the Multiplicative Weights algorithm. These might be of independent interest.