标题： 基于模型的定价方法在数据市场中的机器学习应用 显示英文标题

标题： Model-based Pricing for Machine Learning in a Data Marketplace

摘要： 数据挖掘使用机器学习（ML）在科学、商业智能、新闻报道和其他许多领域已经成为普遍现象。 虽然很多工作集中在降低机器学习模型的训练成本、推理运行时间和存储成本，但很少有工作研究如何降低数据获取的成本，这可能会导致卖家收入的损失和买家的负担能力和效率的下降。 在本文中，我们提出了一种基于模型的定价（MBP）框架，该框架不是对数据进行定价，而是直接对机器学习模型实例进行定价。 我们首先正式描述了MBP框架所需的特性，重点是避免套利。 接下来，我们通过一种噪声注入方法展示了MBP框架的具体实现，该方法可以证明满足所需的正式特性。 基于所提出的框架，我们随后提供了算法解决方案，说明卖方如何在不同的市场情景（如最大化收入）下为模型分配价格。 最后，我们进行了广泛的实验，结果表明MBP框架可以为卖方提供高收入，为买方提供高可负担性，并且可以在低运行时间成本下运行。 显示英文摘要

摘要： Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.