5 papers
Identifying Approximate Minimizers under Stochastic Uncertainty
Hessa Al-Thani, Viswanath Nagarajan
We study a fundamental stochastic selection problem involving independent random variables, each of which can be queried at some cost. Given a tolerance level , the goal is…
Sparse Graphical Designs via Linear Programming
Hessa Al-Thani, Catherine Babecki, J. Carlos Martínez Mori
Graphical designs are a framework for sampling and numerical integration of functions on graphs. In this note, we introduce a method to address the trade-off between graphical desi…
Minimum Cost Adaptive Submodular Cover
Hessa Al-Thani, Yubing Cui, Viswanath Nagarajan
Adaptive submodularity is a fundamental concept in stochastic optimization, with numerous applications such as sensor placement, hypothesis identification and viral marketing. We c…
Tridiagonal Maximum-Entropy Sampling and Tridiagonal Masks
Hessa Al-Thani, Jon Lee
The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-)determinant principal submatrix, of a given order, from an input covariance matrix . We give an efficie…
An R Package for generating covariance matrices for maximum-entropy sampling from precipitation chemistry data
Hessa Al-Thani, Jon Lee
We present an open-source R package (MESgenCov v 0.1.0) for temporally fitting multivariate precipitation chemistry data and extracting a covariance matrix for use in the MESP (max…