2 citations · 3 across the 4 of their papers we have counts for
7 papers
Bloom Origami Assays: Practical Group Testing
Louis Abraham, Gary Becigneul, Benjamin Coleman +3
We study the problem usually referred to as group testing in the context of COVID-19. Given n samples collected from patients, how should we select and test mixtures of samples to…
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge
Benjamin Coleman, Gaurav Gupta, John Chen +1
Empirical risk minimization is perhaps the most influential idea in statistical learning, with applications to nearly all scientific and technical domains in the form of regression…
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…
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…