[2603.04224] Nearest-Neighbor Density Estimation for Dependency Suppression

Computer Science > Machine Learning arXiv:2603.04224 (cs) [Submitted on 4 Mar 2026] Title:Nearest-Neighbor Density Estimation for Dependency Suppression Authors:Kathleen Anderson, Thomas Martinetz View a PDF of the paper titled Nearest-Neighbor Density Estimation for Dependency Suppression, by Kathleen Anderson and 1 other authors View PDF HTML (experimental) Abstract:The ability to remove unwanted dependencies from data is crucial in various domains, including fairness, robust learning, and privacy protection. In this work, we propose an encoder-based approach that learns a representation independent of a sensitive variable but otherwise preserving essential data characteristics. Unlike existing methods that rely on decorrelation or adversarial learning, our approach explicitly estimates and modifies the data distribution to neutralize statistical dependencies. To achieve this, we combine a specialized variational autoencoder with a novel loss function driven by non-parametric nearest-neighbor density estimation, enabling direct optimization of independence. We evaluate our approach on multiple datasets, demonstrating that it can outperform existing unsupervised techniques and even rival supervised methods in balancing information removal and utility. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2603.04224 [cs.LG]   (or arXiv:2603.04224v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.04224 Focus to le...