Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed ...

This document is designed to help users quickly understand, use, and maintain the Python implementation of the Matrix-Sparsity-Based Pauli Decomposition (MSPD) algorithm. It specifies the function, ...

Fear Factor is back — with a Big Brother-y twist. Entertainment Weekly can exclusively premiere the first trailer for Fear Factor: House of Fear, the new iteration of the skin-crawling reality ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States ...