6 citations · 6 across the 4 of their papers we have counts for
4 papers
Robust high-dimensional Bayesian regression with non-Gaussian errors under global--local shrinkage priors
Mohammad Arashi
Multivariate regression with many correlated responses and predictors commonly violates Gaussian error assumptions due to heavy tails, outliers, and asymmetry. Gaussian procedures…
Stein-Rule Shrinkage for Stochastic Gradient Estimation in High Dimensions
M. Arashi, M. Amintoosi
Stochastic gradient methods are central to large-scale learning, but they treat mini-batch gradients as unbiased estimators, which classical decision theory shows are inadmissible…
Adaptive Bayesian Structure Learning of DAGs With Non-conjugate Prior
S. Nazari, M. Arashi, A. Sadeghkhani
Directed Acyclic Graphs (DAGs) are solid structures used to describe and infer the dependencies among variables in multivariate scenarios. Having a thorough comprehension of the ac…
Big Data Analysis Using Shrinkage Strategies
B. Yuzbasi, M. Arashi, S. E. Ahmed
In this paper, we apply shrinkage strategies to estimate regression coefficients efficiently for the high-dimensional multiple regression model, where the number of samples is smal…