1.3k citations · 2.1k across the 14 of their papers we have counts for
Showing 2016Show all
3 papers · 1 filter
stat.ME2016★ 5 cited
A convex framework for high-dimensional sparse Cholesky based covariance estimation
Kshitij Khare, Sang Oh, Syed Rahman +1
Covariance estimation for high-dimensional datasets is a fundamental problem in modern day statistics with numerous applications. In these high dimensional datasets, the number of…
stat.ME2016★ 7 cited
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
Alnur Ali, Kshitij Khare, Sang-Yun Oh +1
We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, thro…
stat.ML2016★ 19 cited
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Evan Racah, Seyoon Ko, Peter Sadowski +5
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientis…