Motivated by the emergence of decentralized machine learning ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two fundamental machine learning challenges: lack of certainty in the assessment of model quality and lack of knowledge regarding the optimal performance of any model. We show that lack of certainty can be dealt with via simple linear contracts that achieve 1-1/e fraction of the first-best utility, even if the principal has a small test set. Furthermore, we give sufficient conditions on the size of the principal's test set that achieves a vanishing additive approximation to the optimal utility. To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract.

通过对去中心化机器学习生态系统的兴起的研究，我们研究了数据采集的委派。以契约理论为出发点，我们设计了能够处理两个基本机器学习挑战的最优和近似最优契约：模型质量评估的不确定性和关于任何模型最优性能的缺乏知识。我们展示了可以通过简单的线性契约解决不确定性问题，即使主体只有一个小的测试集合，也能达到第一优的收益的1-1/e的分数。此外，我们给出了主体测试集合大小的足够条件，以获得对最优效用的逼近。为了解决对最优性能缺乏先验知识的问题，我们提出了一个能够自适应高效计算最优契约的凸规划方法。

分布式机器学习中的数据收集委派