# Efficient Tensor Decomposition

arXiv: https://arxiv.org/abs/2007.15589 · HTML: https://mlsanity.com/paper/2007.15589/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Learning latent causal graphs via mixture oracles](https://mlsanity.com/paper/2106.15563/) (2021)
- [Overcomplete Tensor Decomposition via Koszul-Young Flattenings](https://mlsanity.com/paper/2411.14344/) (2024)

## Abstract (arXiv)

This chapter studies the problem of decomposing a tensor into a sum of constituent rank one tensors. While tensor decompositions are very useful in designing learning algorithms and data analysis, they are NP-hard in the worst-case. We will see how to design efficient algorithms with provable guarantees under mild assumptions, and using beyond worst-case frameworks like smoothed analysis.