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First Proof Solutions and Comments

M. Abouzaid, A. J. Blumberg, M. Hairer, J. Kileel, T. G. Kolda, P. D. Nelson, D. Spielman, N. Srivastava, R. Ward, S. Weinberger and L. Williams, , 2026

Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data

B. W. Larsen, T. G. Kolda, A. R. Zhang and A. H. Williams, , 2024

Tensor Moments of Gaussian Mixture Models: Theory and Applications

J. M. Pereira, J. Kileel and T. G. Kolda, , 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

XPCA: Extending PCA for a Combination of Discrete and Continuous Variables

C. Anderson-Bergman, T. G. Kolda and K. Kincher-Winoto, arXiv, 2018

Symmetric Orthogonal Tensor Decomposition is Trivial

T. G. Kolda, arXiv, 2015

Accelerating Community Detection by Using K-core Subgraphs

C. Peng, T. G. Kolda and A. Pinar, arXiv, 2014

Making Tensor Factorizations Robust to Non-Gaussian Noise

E. C. Chi and T. G. Kolda, Tech. Rep., Sandia National Laboratories, 2011

Poblano v1.0: A Matlab Toolbox for Gradient-Based Optimization

D. M. Dunlavy, T. G. Kolda and E. Acar, Tech. Rep., Sandia National Laboratories, 2010