A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression. - Elizaveta Rebrova; Gustavo Chavez; Yang Liu 179; Pieter Ghysels; Xiaoye Sherry Li (IPDPS Workshops, 2018)

Sketching for Motzkin's Iterative Method for Linear Systems. - Elizaveta Rebrova; Deanna Needell (ACSSC, 2019)

Stochastic Gradient Descent Variants for Corrupted Systems of Linear Equations. - Jamie Haddock; Deanna Needell; Elizaveta Rebrova; William Swartworth (CISS, 2020)

COVID-19 Literature Topic-Based Search via Hierarchical NMF. - Rachel Grotheer; Longxiu Huang; Yihuan Huang; Alona Kryshchenko; Oleksandr Kryshchenko; Pengyu Li; Xia Li; Elizaveta Rebrova; Kyung Ha; Deanna Needell (NLP4COVID@EMNLP, 2020)

Scalable and Memory-Efficient Kernel Ridge Regression. - Gustavo Chávez; Yang Liu 179; Pieter Ghysels; Xiaoye Sherry Li; Elizaveta Rebrova (IPDPS, 2020)

On Nonnegative Matrix and Tensor Decompositions for COVID-19 Twitter Dynamics. - Lara Kassab; Alona Kryshchenko; Hanbaek Lyu; Denali Molitor; Deanna Needell; Elizaveta Rebrova (CoRR, 2020)

A parallel hierarchical blocked adaptive cross approximation algorithm. - Yang Liu 179; Wissam M. Sid-Lakhdar; Elizaveta Rebrova; Pieter Ghysels; Xiaoye Sherry Li (Int. J. High Perform. Comput. Appl., 2020)

On a Guided Nonnegative Matrix Factorization. - Joshua Vendrow; Jamie Haddock; Elizaveta Rebrova; Deanna Needell (ICASSP, 2021)

Analysis of Legal Documents via Non-negative Matrix Factorization Methods. - Ryan Budahazy; Lu Cheng 005; Yihuan Huang; Andrew Johnson; Pengyu Li; Joshua Vendrow; Zhoutong Wu; Denali Molitor; Elizaveta Rebrova; Deanna Needell (CoRR, 2021)

Nonlinear Matrix Approximation with Radial Basis Function Components. - Elizaveta Rebrova; Yu-Hang Tang (CoRR, 2021)

Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery. - Mark A. Iwen; Deanna Needell; Michael Perlmutter; Elizaveta Rebrova (CoRR, 2021)

On block Gaussian sketching for the Kaczmarz method. - Elizaveta Rebrova; Deanna Needell (Numer. Algorithms, 2021)

Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares. - Mark A. Iwen; Deanna Needell; Elizaveta Rebrova; Ali Zare (SIAM J. Matrix Anal. Appl., 2021)

On Block Accelerations of Quantile Randomized Kaczmarz for Corrupted Systems of Linear Equations. - Lu Cheng 005; Benjamin Jarman; Deanna Needell; Elizaveta Rebrova (CoRR, 2022)

Quantile-Based Iterative Methods for Corrupted Systems of Linear Equations. - Jamie Haddock; Deanna Needell; Elizaveta Rebrova; William Swartworth (SIAM J. Matrix Anal. Appl., 2022)

On Subsampled Quantile Randomized Kaczmarz. - Jamie Haddock; Anna Ma; Elizaveta Rebrova (Allerton, 2023)

On Graph Uncertainty Principle and Eigenvector Delocalization. - Elizaveta Rebrova; Palina Salanevich (CoRR, 2023)

Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements. - Cullen Haselby; Mark A. Iwen; Deanna Needell; Elizaveta Rebrova; William Swartworth (CoRR, 2023)

A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation. - Jackie Lok; Elizaveta Rebrova (CoRR, 2023)

Stochastic gradient descent for streaming linear and rectified linear systems with Massart noise. - Halyun Jeong; Deanna Needell; Elizaveta Rebrova (CoRR, 2024)

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems. - Michal Derezinski; Daniel LeJeune; Deanna Needell; Elizaveta Rebrova (CoRR, 2024)

Discrete error dynamics of mini-batch gradient descent for least squares regression. - Jackie Lok; Rishi Sonthalia; Elizaveta Rebrova (CoRR, 2024)

On Regularization via Early Stopping for Least Squares Regression. - Rishi Sonthalia; Jackie Lok; Elizaveta Rebrova (CoRR, 2024)

Learning nonnegative matrix factorizations from compressed data. - Abraar Chaudhry; Elizaveta Rebrova (CoRR, 2024)

Sparseness-constrained nonnegative tensor factorization for detecting topics at different time scales. - Lara Kassab; Alona Kryshchenko; Hanbaek Lyu; Denali Molitor; Deanna Needell; Elizaveta Rebrova; Jiahong Yuan (Frontiers Appl. Math. Stat., 2024)

Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition. - Michal Derezinski; Elizaveta Rebrova (SIAM J. Math. Data Sci., 2024)

Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression. - Jackie Lok; Rishi Sonthalia; Elizaveta Rebrova (ALT, 2025)

Randomized Kaczmarz Methods with Beyond-Krylov Convergence. - Michal Derezinski; Deanna Needell; Elizaveta Rebrova; Jiaming Yang (CoRR, 2025)

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery. - Shambhavi Suryanarayanan; Elizaveta Rebrova (CoRR, 2025)

Data-Driven, ML-assisted Approaches to Problem Well-Posedness. - Tom Bertalan; George A. Kevrekidis; Eleni D. Koronaki; Siddhartha Mishra; Elizaveta Rebrova; Yannis G. Kevrekidis (CoRR, 2025)

Subspace-constrained randomized coordinate descent for linear systems with good low-rank matrix approximations. - Jackie Lok; Elizaveta Rebrova (CoRR, 2025)

Scientific Applications Leveraging Randomized Linear Algebra. - Vivak Patel; Daniel Adrian Maldonado; Maksim Melnichenko; Nathaniel Pritchard; Vishwas Rao; Elizaveta Rebrova; Sriram Sankararaman (CoRR, 2025)