Quantifying the value of data within a machine learning workflow can play a
pivotal role in making more strategic decisions in machine learning
initiatives. The existing Shapley value based frameworks for data valuation in
machine learning are computationally expensive as they require considerable
amount of repeated training of the model to obtain the Shapley value. In this
paper, we introduce an efficient data valuation framework EcoVal, to estimate
the value of data for machine learning models in a fast and practical manner.
Instead of directly working with individual data sample, we determine the value
of a cluster of similar data points. This value is further propagated amongst
all the member cluster points. We show that the overall data value can be
determined by estimating the intrinsic and extrinsic value of each data. This
is enabled by formulating the performance of a model as a \textit{production
function}, a concept which is popularly used to estimate the amount of output
based on factors like labor and capital in a traditional free economic market.
We provide a formal proof of our valuation technique and elucidate the
principles and mechanisms that enable its accelerated performance. We
demonstrate the real-world applicability of our method by showcasing its
effectiveness for both in-distribution and out-of-sample data. This work
addresses one of the core challenges of efficient data valuation at scale in
machine learning models.

我们介绍了一种高效数据估值框架 EcoVal，通过确定类似数据点的群集的价值，并在其成员间传播，以快速而实用的方式估计机器学习模型的数据价值。我们将模型的性能建模为一个生产函数，通过估计每个数据的内在和外在价值来确定整体数据价值。我们通过提供正式证明和阐明加速性能的原理和机制，展示了该方法在实际应用中对于分布内和样本外数据的有效性。该研究解决了机器学习模型中规模化高效数据估值的核心挑战之一。