{"ID":2830730,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09665","arxiv_id":"2512.09665","title":"OxEnsemble: Fair Ensembles for Low-Data Classification","abstract":"We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are common in domains like medical imaging, where false negatives can have fatal consequences. We propose a novel approach \\emph{OxEnsemble} for efficiently training ensembles and enforcing fairness in these low-data regimes. Unlike other approaches, we aggregate predictions across ensemble members, each trained to satisfy fairness constraints. By construction, \\emph{OxEnsemble} is both data-efficient -- carefully reusing held-out data to enforce fairness reliably -- and compute-efficient, requiring little more compute than used to fine-tune or evaluate an existing model. We validate this approach with new theoretical guarantees. Experimentally, our approach yields more consistent outcomes and stronger fairness-accuracy trade-offs than existing methods across multiple challenging medical imaging classification datasets.","short_abstract":"We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are common in domains like medical imaging, where false negatives can have fatal consequences. We propose a novel approach \\emph{OxEnsemble} for efficiently training ensembles a...","url_abs":"https://arxiv.org/abs/2512.09665","url_pdf":"https://arxiv.org/pdf/2512.09665v2","authors":"[\"Jonathan Rystrøm\",\"Zihao Fu\",\"Chris Russell\"]","published":"2025-12-10T14:08:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CY\",\"cs.LG\"]","methods":"[]","has_code":false}