先试后买：面向真实世界数据市场的实用数据购买算法
Try Before You Buy: A practical data purchasing algorithm for real-world data marketplaces
AI总结：
针对数据市场中买家难以预先评估数据集价值的问题，提出TBYB算法，利用单个数据集性能度量，以O(N)信息实现接近完全信息模型的购买性能。
摘要：
数据交易正变得越来越流行，这一点从过去几年中涌现的大量数据市场（DMs）可以看出。数字资产的定价尤为复杂，因为与实物资产不同，数字资产可以以零成本复制、几乎免费地存储和传输等。在大多数数据市场中，数据卖家被邀请标明价格，并附上其数据集的描述。然而，对于数据买家来说，决定支付所要求的价格是否合理，只能在他们使用数据运行其AI/ML算法之后才能进行。理论工作已经在完全信息模型的背景下分析了购买哪些数据集以及以什么价格购买的问题，在该模型中，算法在N个数据集的O(2^N)个可能子集上的性能是预先已知的，同时买家的价值函数也是已知的。然而，这种信息难以计算，更不用说在真实世界的数据市场中公开了。\n 在本文中，我们表明，如果数据市场向潜在买家提供其AI/ML算法在单个数据集上的性能度量，那么他们就可以选择购买哪些数据集，其有效性接近于完全信息模型。我们将由此产生的算法称为“先试后买”（TBYB），并在合成和真实世界数据集上展示，TBYB如何仅通过市场发布的O(N)而非O(2^N)的信息，就能实现接近最优的购买性能。
英文摘要：
Data trading is becoming increasingly popular, as evident by the appearance of scores of Data Marketplaces (DMs) in the last few years. Pricing digital assets is particularly complex since, unlike physical assets, digital ones can be replicated at zero cost, stored, and transmitted almost for free, etc. In most DMs, data sellers are invited to indicate a price, together with a description of their datasets. For data buyers, however, deciding whether paying the requested price makes sense, can only be done after having used the data with their AI/ML algorithms. Theoretical works have analysed the problem of which datasets to buy, and at what price, in the context of full information models, in which the performance of algorithms over any of the O(2^N) possible subsets of N datasets is known a priori, together with the value functions of buyers. Such information is, however, difficult to compute, let alone be made public in the context of real-world DMs. In this paper, we show that if a DM provides to potential buyers a measure of the performance of their AI/ML algorithm on individual datasets, then they can select which datasets to buy with an efficacy that approximates that of a complete information model. We call the resulting algorithm Try Before You Buy (TBYB) and demonstrate over synthetic and real-world datasets how TBYB can lead to near optimal buying performance with only O(N) instead of O(2^N) information released by a marketplace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
