Abstract Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …

As image-based deep learning becomes pervasive on every device, from cell phones to
smart watches, there is a growing need to develop methods that continually learn from data …

Federated learning (FL) is a popular technique to train machine learning (ML) models on
decentralized data sources. In order to sustain long-term participation of data owners, it is …

Data valuation has wide use cases in machine learning, including improving data quality
and creating economic incentives for data sharing. This paper studies the robustness of data …

In recent years, data has become one of the most important resources in the digital
economy. Unlike traditional resources, the digital nature of data makes it difficult to value …

Recent years have witnessed a surge of interest in develo** trustworthy methods to
evaluate the value of data in many real-world applications (eg, collaborative machine …

Data Shapley has recently been proposed as a principled framework to quantify the
contribution of individual datum in machine learning. It can effectively identify helpful or …

Due to stricter data management regulations such as General Data Protection Regulation
(GDPR), traditional production mode of machine learning services is shifting to federated …

Data are invaluable. How can we assess the value of data objectively, systematically and
quantitatively? Pricing data, or information goods in general, has been studied and practiced …

Good models require good training data. For overparameterized deep models, the causal
relationship between training data and model predictions is increasingly opaque and poorly …