59 citations · 220 across the 52 of their papers we have counts for
5 papers · 1 filter
Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation
Julianne Chung, Scot M. Miller, Malena Sabate Landman +1
In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are…
Stable Rank and Intrinsic Dimension of Real and Complex Matrices
Ilse C. F. Ipsen, Arvind K. Saibaba
The notion of `stable rank' of a matrix is central to the analysis of randomized matrix algorithms, covariance estimation, deep neural networks, and recommender systems. We compare…
Parametric kernel low-rank approximations using tensor train decomposition
Abraham Khan, Arvind K. Saibaba
Computing low-rank approximations of kernel matrices is an important problem with many applications in scientific computing and data science. We propose methods to efficiently appr…
Bayesian D-Optimal Experimental Designs via Column Subset Selection
Srinivas Eswar, Vishwas Rao, Arvind K. Saibaba
This paper tackles optimal sensor placement for Bayesian linear inverse problems, a popular version of the more general Optimal Experimental Design (OED) problem, using the D-optim…
Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation
Amit N. Subrahmanya, Vishwas Rao, Arvind K. Saibaba
The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to com…