From the 1 of 6 linked papers with an AI index.
6 papers
Bayesian Graphical Models under Positivity Constraints: A Scalable generalized likelihood Approach
Swarnali Raha, Partha Sarkar, Sirani Perera +1
The paper proposes a scalable Bayesian method for estimating precision matrices in Gaussian graphical models with total positivity constraints, using a D‑trace loss and spike‑and‑s…
Optimality of Sub-network Laplace Approximations: New Results and Methods
Swarnali Raha, Kshitij Khare, Rohit K Patra
Although the Laplace approximation offers a simple route to uncertainty quantification in deep neural networks, its reliance on inverting large Hessian matrices has motivated a ran…
Moment bounds for condition numbers and singular values of high-dimensional Gaussian random matrices: Applications and limitations
Partha Sarkar, Kshitij Khare, Sanvesh Srivastava
Spectral properties of Gram matrices are central to high dimensional asymptotic analyses of statistical estimators in regression and covariance estimation. These properties, in tur…
CoMET: A Compressed Bayesian Mixed-Effects Model for High-Dimensional Tensors
Sreya Sarkar, Kshitij Khare, Sanvesh Srivastava
Mixed-effects models are fundamental tools for analyzing clustered and repeated-measures data, but existing high-dimensional methods largely focus on penalized estimation with vect…
High-Dimensional Bernstein Von-Mises Theorems for Covariance and Precision Matrices
Partha Sarkar, Kshitij Khare, Malay Ghosh +1
This paper aims to examine the characteristics of the posterior distribution of covariance/precision matrices in a "large , large " scenario, where represents the number…
High dimensional convergence rates for sparse precision estimators for matrix-variate data
Hongqiang Sun, Kshitij Khare
In several applications, the underlying structure of the data allows for the samples to be organized into a matrix variate form. In such settings, the underlying row and column cov…