ABSTRACT

Data are essential ingredients for building machine learning (ML) applications. The ability to quantify and measure the value of data is critical to the entire ML lifecycle: from identifying useful data sources, to setting propriety over samples during training, and to interpreting the reason why certain behaviors of a model emerge during deployment. The potential of data valuation has been observed in many applications over the past few years. However, intermixed with these positive results is a vast array of applications for which existing data valuation techniques are not yet applicable, or too expensive to execute, or produce valuation results with substantial uncertainty. This project aims to enable data valuation to overcome applicability, scalability, and reproducibility challenges and transition to a practical and reliable tool for a data-centric future. This work will have a broad impact on society in terms of facilitating automated data quality management, designing incentives for data sharing, and improving the robustness of ML applications. This project will train undergraduate students to solve ML problems from both an algorithmic and a data quality perspective, while in the meantime creating useful school-age learning modules implemented at local, regional, and global scales.

The project consists of four research tasks to advance data valuation from different dimensions: 1) designing data valuation techniques that are robust to overcome the randomness in modern ML training algorithms; 2) developing new frameworks to determine the value of data samples given limited information about downstream learning tasks; 3) investigating principled methods to value heterogeneous and streaming data; and 4) creating and open-sourcing a unified multi-faceted evaluation platform to spur future advances in more complex data valuation. The proposed techniques are implemented and validated on a variety of high-impact real-world applications, including autonomous driving, energy-efficient buildings, and conversational artificial intelligence.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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