# Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation

arXiv: https://arxiv.org/abs/2507.20542 · HTML: https://mlsanity.com/paper/2507.20542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Improving Recommendation Fairness via Data Augmentation](https://mlsanity.com/paper/2302.06333/) (2023)
- [TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges](https://mlsanity.com/paper/2501.06647/) (2025)
- [TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records](https://mlsanity.com/paper/1911.05843/) (2019)
- [Influence-guided Data Augmentation for Neural Tensor Completion](https://mlsanity.com/paper/2108.10248/) (2021)
- [Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization](https://mlsanity.com/paper/2210.09013/) (2022)
- [Fair-CDA: Continuous and Directional Augmentation for Group Fairness](https://mlsanity.com/paper/2304.00295/) (2023)
- [Learning Fair Canonical Polyadical Decompositions using a Kernel Independence Criterion](https://mlsanity.com/paper/2104.13504/) (2021)
- [Towards a Fairer Non-negative Matrix Factorization](https://mlsanity.com/paper/2411.09847/) (2024)

## Abstract (arXiv)

Group fairness is important to consider in tensor decomposition to prevent discrimination based on social grounds such as gender or age. Although few works have studied group fairness in tensor decomposition, they suffer from performance degradation. To address this, we propose STAFF(Sparse Tensor Augmentation For Fairness) to improve group fairness by minimizing the gap in completion errors of different groups while reducing the overall tensor completion error. Our main idea is to augment a tensor with augmented entities including sufficient observed entries to mitigate imbalance and group bias in the sparse tensor. We evaluate method on tensor completion with various datasets under conventional and deep learning-based tensor models. STAFF consistently shows the best trade-off between completion error and group fairness; at most, it yields 36% lower MSE and 59% lower MADE than the second-best baseline.