arXiv Machine Learning September 17, 2026 By Yuanbo Guo, Jun Xia, Yiyu Shi

FairLRF: Achieving Fairness through Sparse Low Rank Factorization

Read the original on arXiv Machine Learning →

FairLRF proposes a fairness-oriented low rank factorization framework that uses singular value decomposition (SVD) to improve deep learning model fairness. By selectively removing bias-inducing elements from the unitary matrices obtained via SVD, the method reduces group disparities while preserving accuracy. Experiments demonstrate that FairLRF outperforms existing low rank factorization and state-of-the-art fairness techniques, and an ablation study explores the impact of key hyper-parameters.

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