435 citations · 1.7k across the 22 of their papers we have counts for
4 papers · 1 filter
Using Machine Learning for Discovery in Synoptic Survey Imaging
Henrik Brink, Joseph W. Richards, Dovi Poznanski +4
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact t…
Rank Centrality: Ranking from Pair-wise Comparisons
Sahand Negahban, Sewoong Oh, Devavrat Shah
The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers…
Scaling Multiple-Source Entity Resolution using Statistically Efficient Transfer Learning
Sahand Negahban, Benjamin I. P. Rubinstein, Jim Gemmell
We consider a serious, previously-unexplored challenge facing almost all approaches to scaling up entity resolution (ER) to multiple data sources: the prohibitive cost of labeling…
Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
We develop and analyze stochastic optimization algorithms for problems in which the expected loss is strongly convex, and the optimum is (approximately) sparse. Previous approaches…