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

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...

The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data ...

SIAM Journal on Applied Mathematics, Vol. 78, No. 6 (2018), pp. 3279-3295 (17 pages) Extracting governing equations from dynamic data is an essential task in model selection and parameter estimation.

This book offers a comprehensive framework for mastering the complexities of learning high-dimensional sparse graphical models through the use of conditional independence tests. These tests are ...