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Population-Robust Feature Selection via Generalized Welfare Optimization — arXiv:2608.02887

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.02887
License (as archived): CC0-1.0
Archived: 2026-10-31T12:42:16.763Z
SHA-256 of the archived text: ca6abb2a4f39feae8ebb525d826a73370e5b53b91a7685aa071b62e0ee6080b8
Stable URL of this page: https://metraipolis.com/documento/dc_3d64d55fb79f

How to cite

Population-Robust Feature Selection via Generalized Welfare Optimization — arXiv:2608.02887 — arXiv (en), https://arxiv.org/abs/2608.02887 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3d64d55fb79f (Metraipolis archive dc_3d64d55fb79f, sha256 ca6abb2a4f39feae8ebb525d826a73370e5b53b91a7685aa071b62e0ee6080b8)

BibTeX

@misc{dc3d64d55fb79f,
  title = {Population-Robust Feature Selection via Generalized Welfare Optimization — arXiv:2608.02887},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.02887},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3d64d55fb79f; sha256 ca6abb2a4f39feae8ebb525d826a73370e5b53b91a7685aa071b62e0ee6080b8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.02887. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Choosing which features to collect is a deployment decision: the same limited questionnaire, test panel, or sensor set may need to serve several heterogeneous populations. Standard feature-selection methods typically optimize for one large population, while existing robust approaches tend to learn one shared model for every population. We introduce PopFS, a method for learning one shared, deployable feature set that is robust to population differences while letting each pop- ulation train its own model. PopFS uses a tunable welfare objective that lets practitioners balance overall predictive ben- efit against stronger protection of the populations that benefit least. To make this objective practical at scale, PopFS first uses multitask sparse learning to reduce the candidate pool, then searches directly over hard feature sets by ranking promising additions and swaps and fully refitting only a shortlist. Across eight population splits from six prediction tasks drawn from five tabular and public-health datasets, PopFS consistently achieves strong average and worst-population performance while scaling to thousands of candidate features. A 43-state COVID-19 nowcasting study further shows that changing the welfare objective can improve the least-served states with lit- tle change in average performance and yields an interpretable change in the selected symptom signals. Our code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.02887 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:31:30.325Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).