Convergence of Alternating Gradient Descent for Matrix Factorization

R. Ward and T. G. Kolda, In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023

Streaming Generalized Canonical Polyadic Tensor Decompositions

E. T. Phipps, N. T. Johnson and T. G. Kolda, In Proceedings of Platform for Advanced Scientific Computing (PASC’23) Conference, 2023

Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

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

Tensors Methods in Statistics

Had a great visit to University of Chicago, a highlight of which was getting a signed copy of the 2nd edition of Tensor Methods in Statistics.

Tensor Moments of Gaussian Mixture Models: Theory and Applications

J. M. Pereira, J. Kileel and T. G. Kolda, , 2022

Will the real Jennrich's Algorithm please stand up?

In many papers on tensor decomposition since 2014, the simultaneous diagonalization algorithm is incorrectly referenced as Jennrich’s algorithm. This method should not be attributed to Jennrich but instead cited as Leurgans, Ross, and Abel (1993).

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

Estimating Higher-Order Moments Using Symmetric Tensor Decomposition

S. Sherman and T. G. Kolda, SIAM Journal on Matrix Analysis and Applications, 2020

TuckerMPI: A Parallel C++/MPI Software Package for Large-scale Data Compression via the Tucker Tensor Decomposition

G. Ballard, A. Klinvex and T. G. Kolda, ACM Transactions on Mathematical Software, 2020