5 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.DB2020
Storyboard: Optimizing Precomputed Summaries for Aggregation
Edward Gan, Peter Bailis, Moses Charikar
An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggre…
cs.LG2019★ 5 cited
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Justin Chen, Edward Gan, Kexin Rong +2
Domain adaptation provides a powerful set of model training techniques given domain-specific training data and supplemental data with unknown relevance. The techniques are useful w…
cs.DB2018★ 3 cited
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Edward Gan, Jialin Ding, Kai Sheng Tai +2
Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable…