Randomized Algorithms

Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

B. W. Larsen and T. G. Kolda, SIAM J. Matrix Analysis and Applications, 2022

Sketching Matrix Least Squares via Leverage Scores Estimates

B. W. Larsen and T. G. Kolda, , 2022

Randomized Algorithms for Scientific Computing (RASC)

A. Buluc, T. G. Kolda, S. M. Wild, M. Anitescu, A. DeGennaro, J. Jakeman, C. Kamath, R. Kannan, M. E. Lopes, P.-G. Martinsson, K. Myers, J. Nelson, J. M. Restrepo, C. Seshadhri, D. Vrabie, B. Wohlberg, S. J. Wright, C. Yang and P. Zwart, , 2021

Stochastic Gradients for Large-Scale Tensor Decomposition

T. G. Kolda and D. Hong, SIAM Journal on Mathematics of Data Science, 2020

Faster Johnson-Lindenstrauss Transforms via Kronecker Products

R. Jin, T. G. Kolda and R. Ward, Information and Inference: A Journal of the IMA, 2020

A Practical Randomized CP Tensor Decomposition

C. Battaglino, G. Ballard and T. G. Kolda, SIAM Journal on Matrix Analysis and Applications, 2018

Diamond Sampling for Approximate Maximum All-pairs Dot-product (MAD) Search

G. Ballard, A. Pinar, T. G. Kolda and C. Seshadri, In ICDM 2015: Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Wedge Sampling for Computing Clustering Coefficients and Triangle Counts on Large Graphs

C. Seshadhri, A. Pinar and T. G. Kolda, Statistical Analysis and Data Mining, 2014

Triadic Measures on Graphs: The Power of Wedge Sampling

C. Seshadhri, A. Pinar and T. G. Kolda, In SDM13: Proceedings of the 2013 SIAM International Conference on Data Mining, 2013