9 citations · 16 across the 5 of their papers we have counts for
5 papers
Nonparametric Bayesian Knockoff Generators for Feature Selection Under Complex Data Structure
Michael J. Martens, Anjishnu Banerjee, Xinran Qi +1
The recent proliferation of high-dimensional data, such as electronic health records and genetics data, offers new opportunities to find novel predictors of outcomes. Presented wit…
Radiomic features of multi-parametric MRI present stable associations with analogous histological features in brain cancer patients
Samuel Bobholz, Allison Lowman, Alexander Barrington +9
MR-derived radiomic features have demonstrated substantial predictive utility in modeling different prognostic factors of glioblastomas and other brain cancers. However, the biolog…
Parallel inversion of huge covariance matrices
Anjishnu Banerjee, Joshua Vogelstein, David Dunson
An extremely common bottleneck encountered in statistical learning algorithms is inversion of huge covariance matrices, examples being in evaluating Gaussian likelihoods for a larg…
Bayesian learning of joint distributions of objects
Anjishnu Banerjee, Jared Murray, David B. Dunson
There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types…
Efficient Gaussian Process Regression for Large Data Sets
Anjishnu Banerjee, David Dunson, Surya Tokdar
Gaussian processes (GPs) are widely used in nonparametric regression, classification and spatio-temporal modeling, motivated in part by a rich literature on theoretical properties.…