Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

JACOB BIEN, ROBERT J. TIBSHIRANI, Sparse estimation of a covariance matrix, Biometrika, Vol. 98, No. 4 (DECEMBER 2011), pp. 807-820 ...

For dimensions p that can be much larger than the sample size n, we consider the estimation of sparse vectors θ*. Under matrix uncertainty, the Lasso and Dantzig selector turn out to be extremely ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...