3 papers
stat.ML2020
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…
cs.LG2019
Negative sampling in semi-supervised learning
John Chen, Vatsal Shah, Anastasios Kyrillidis
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of ne…
cs.DS2019
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…