Randomized numerical linear algebra:

  • A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation
    ... Jackie Lok, Elizaveta Rebrova
    ... submitted, arXiv:2309.04889
  • On Subsampled Quantile Randomized Kaczmarz
    ... Jamie Haddock, Anna Ma, Elizaveta Rebrova
    ... Proc. Allerton Conf. on Communication, Control, and Computing, Monticello, IL (2023), arXiv:2308.07987
  • Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
    ... Michal Derezinski, Elizaveta Rebrova
    ... SIAM Journal on Mathematics of Data Science (SIMODS), to appear, or arXiv:2208.09585
  • On Block Accelerations of Quantile Randomized Kaczmarz for Corrupted Systems of Linear Equations
    ... Lu Cheng, Benjamin Jarman, Deanna Needell, Elizaveta Rebrova
    ... Inverse Problems, arXiv:2206.12554
  • Quantile-based Iterative Methods for Corrupted Systems of Linear Equations
    ... J. Haddock, D. Needell, E. Rebrova, W. Swartworth
    ... SIAM Journal on Matrix Analysis and Applications (SIMAX), Vol. 43, No. 2, pp. 605--637, arXiv:2009.08089 and journal versions
  • On block Gaussian sketching for the Kaczmarz method
    ... E. Rebrova, D. Needell
    ... Numerical Algorithms 86 (1), 443-473, or arXiv:1905.08894
  • Sketching for Motzkin's iterative method for linear systems
    ... E. Rebrova, D. Needell
    ... Proc. 50th Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA (2019), or arXiv:1912.00771

Tensor data compression and recovery:

  • Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements
    ... C. Haselby, M. Iwen, D. Needell, E. Rebrova, W. Swartworth
    ... submitted, arXiv:2308.13709
  • Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery
    ... C. Haselby, M. Iwen, D. Needell, M. Perlmutter, E. Rebrova
    ... Applied and Computational Harmonic Analysis 66, 161-192, journal or arXiv:2109.10454
  • Lower Memory Oblivious (Tensor) Subspace Embeddings with Fewer Random Bits: Modewise Methods for Least Squares
    ... M. Iwen, D. Needell, E. Rebrova, A. Zare
    ... SIAM Journal on Matrix Analysis and Applications (SIMAX), to appear, or arXiv:1912.08294

Non-asymptotic random matrix theory:

  • Constructive regularization of the random matrix norm
    ... E. Rebrova
    ... Journal of Theoretical Probability, 33(3), pp 1768-1790 (2020), or arXiv:1809.03926
  • Norms of random matrices: local and global problems
    ... E. Rebrova, R. Vershynin
    ... Advances in Mathematics, Vol. 324, pp 40–83 (2018), or arXiv:1608.06953
  • Coverings of random ellipsoids, and invertibility of matrices with i.i.d. heavy-tailed entries
    ... E. Rebrova, K. Tikhomirov
    ... Israel J. Math., Vol. 227(2), pp 507-544 (2018), or arXiv:1508.06690
  • Spectral Properties of Heavy-Tailed Random Matrices (PhD Thesis)
    ... E. Rebrova
    ... University of Michigan (2018), see full text and defense presentation

Data analysis via matrix and tensor factorizations:

  • Sparseness-constrained Nonnegative Tensor Factorization for Detecting Topics at Different Time Scales
    ... L. Kassab, A. Kryschenko, H.Lyu, D. Molitor, D. Needell, E. Rebrova, J. Yuan
    ... submitted, arXiv:2010.01600
  • On A Guided Nonnegative Matrix Factorization
    ... J. Vendrow, J. Haddock, E. Rebrova, D. Needell
    ... Proc. ICASSP IEEE International Conf. on Acoustics, Speech, and Signal Proc. (2021), or arXiv:2010.11365
  • COVID-19 Literature Topic-Based Search via Hierarchical NMF
    ... R. Grotheer, K. Ha, L. Huang, Y. Huang, A. Kryshchenko, O. Kryshchenko, P. Li, X. Li, D. Needell, E. Rebrova
    ... Proc. NLP-COVID19-EMNLP (2020), or link
  • Analysis of Legal Documents via Non-negative Matrix Factorization Methods
    ...R. Budahazy, L. Cheng, Y. Huang, A. Johnson, P. Li, J. Vendrow, Z. Wu, D. Molitor, E. Rebrova, D. Needell
    ... arXiv:2104.14028

Graph signal processing

  • On Graph Uncertainty Principle and Eigenvector Delocalization
    ... E. Rebrova, P. Salanevich
    ... Proc. SAMPTA IEEE Sampling Theory and Applications Conference (2023), link, or arXiv:2306.15810

Scalable linear algebra for machine learning and scientific computing:

  • Scalable and Memory-Efficient Kernel Ridge Regression
    ... G. Chavez, E. Rebrova, Y. Liu, P. Ghysels and X.S.Li
    ... Proc. 34th IPDSP IEEE International Parallel and Distributed Processing Symposium (2020), or link
  • A study of clustering techniques and hierarchical matrix formats for kernel ridge regression
    ... E. Rebrova, G. Chavez, Y. Liu, P. Ghysels and X.S.Li
    ... ParLearning (2018), or arXiv:1803.10274
  • A parallel hierarchical blocked adaptive cross approximation algorithm
    ... Y. Liu, W. Sid-Lakhdar, E. Rebrova, P. Ghysels and X.S.Li
    ... International Journal of High Performance Computing Applications, to appear, or link