5 citations · 10 across the 9 of their papers we have counts for
8 papers · 1 filter
Practical Near Neighbor Search via Group Testing
Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
We present a new algorithm for the approximate near neighbor problem that combines classical ideas from group testing with locality-sensitive hashing (LSH). We reduce the near neig…
Graph Reordering for Cache-Efficient Near Neighbor Search
Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava +1
Graph search is one of the most successful algorithmic trends in near neighbor search. Several of the most popular and empirically successful algorithms are, at their core, a simpl…
Density Sketches for Sampling and Estimation
Aditya Desai, Benjamin Coleman, Anshumali Shrivastava
We introduce Density sketches (DS): a succinct online summary of the data distribution. DS can accurately estimate point wise probability density. Interestingly, DS also provides a…
A One-Pass Private Sketch for Most Machine Learning Tasks
Benjamin Coleman, Anshumali Shrivastava
Differential privacy (DP) is a compelling privacy definition that explains the privacy-utility tradeoff via formal, provable guarantees. Inspired by recent progress toward general-…
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…
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…