1 citations · 1 across the 8 of their papers we have counts for
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Randomized Kaczmarz methods for t-product tensor linear systems with factorized operators
Alejandra Castillo, Jamie Haddock, Iryna Hartsock +7
Randomized iterative algorithms, such as the randomized Kaczmarz method, have gained considerable popularity due to their efficacy in solving matrix-vector and matrix-matrix regres…
Stratified Non-Negative Tensor Factorization
Alexander Sietsema, Zerrin Vural, James Chapman +2
Non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) decompose non-negative high-dimensional data into non-negative low-rank components. NMF and NTF…
Towards a Fairer Non-negative Matrix Factorization
Lara Kassab, Erin George, Deanna Needell +3
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 s…
Stochastic Iterative Methods for Online Rank Aggregation from Pairwise Comparisons
Benjamin Jarman, Lara Kassab, Deanna Needell +1
In this paper, we consider large-scale ranking problems where one is given a set of (possibly non-redundant) pairwise comparisons and the underlying ranking explained by those comp…