112 citations · 133 across the 3 of their papers we have counts for
3 papers
cs.DB2021★ 21 cited
Accelerating Approximate Aggregation Queries with Expensive Predicates
Daniel Kang, John Guibas, Peter Bailis +3
Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using e…
math.ST2021
Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates
Daniel Kang, John Guibas, Peter Bailis +3
Given a dataset , we are interested in computing the mean of a subset of which matches a predicate. ABae leverages stratified sampling and proxy models t…
cs.CV2017★ 112 cited
Synthetic Medical Images from Dual Generative Adversarial Networks
John T. Guibas, Tejpal S. Virdi, Peter S. Li
Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns re…