5 citations · 19 across the 17 of their papers we have counts for
5 papers · 1 filter
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data
Benjamin Coleman, Anshumali Shrivastava
Kernel density estimation is a simple and effective method that lies at the heart of many important machine learning applications. Unfortunately, kernel methods scale poorly for la…
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO)
Gaurav Gupta, Minghao Yan, Benjamin Coleman +5
DNA sequencing, especially of microbial genomes and metagenomes, has been at the core of recent research advances in large-scale comparative genomics. The data deluge has resulted…
RAMBO: Repeated And Merged BloOm Filter for Ultra-fast Multiple Set Membership Testing (MSMT) on Large-Scale Data
Gaurav Gupta, Minghao Yan, Benjamin Coleman +4
Multiple Set Membership Testing (MSMT) is a well-known problem in a variety of search and query applications. Given a dataset of K different sets and a query q, it aims to find all…
Revisiting Consistent Hashing with Bounded Loads
John Chen, Ben Coleman, Anshumali Shrivastava
Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while…
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data
Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava
We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a ske…