# Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion

arXiv: https://arxiv.org/abs/2104.13504 · HTML: https://mlsanity.com/paper/2104.13504/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Fair Kernel Learning](https://mlsanity.com/paper/1710.05578/) (2017)

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

- [Mean Parity Fair Regression in RKHS](https://mlsanity.com/paper/2302.10409/) (2023)
- [Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation](https://mlsanity.com/paper/2507.20542/) (2025)

## Abstract (arXiv)

This work proposes to learn fair low-rank tensor decompositions by regularizing the Canonical Polyadic Decomposition factorization with the kernel Hilbert-Schmidt independence criterion (KHSIC). It is shown, theoretically and empirically, that a small KHSIC between a latent factor and the sensitive features guarantees approximate statistical parity. The proposed algorithm surpasses the state-of-the-art algorithm, FATR (Zhu et al., 2018), in controlling the trade-off between fairness and residual fit on synthetic and real data sets.