Xuehui Sun

Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification?

Quantifying the importance of each training point to a learning task is a fundamental problem in machine learning and the estimated importance scores have been leveraged to guide a range of data workflows such as data summarization and domain adaption. One simple idea is to use the leave-one-out err…