Metraipolis › archive › dc_fe6342de2cc2

Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set — arXiv:2609.11073

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11073
License (as archived): CC0-1.0
Archived: 2026-10-16T05:59:42.592Z
SHA-256 of the archived text: 569c1a29639c3d16dbbcec0d65fbfbacf1f7b47651971c7186ab175a0ad726e1
Stable URL of this page: https://metraipolis.com/documento/dc_fe6342de2cc2

How to cite

Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set — arXiv:2609.11073 — arXiv (en), https://arxiv.org/abs/2609.11073 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fe6342de2cc2 (Metraipolis archive dc_fe6342de2cc2, sha256 569c1a29639c3d16dbbcec0d65fbfbacf1f7b47651971c7186ab175a0ad726e1)

BibTeX

@misc{dcfe6342de2cc2,
  title = {Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set — arXiv:2609.11073},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11073},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fe6342de2cc2; sha256 569c1a29639c3d16dbbcec0d65fbfbacf1f7b47651971c7186ab175a0ad726e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11073. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error. This paper studies latent distributional heterogeneity, where each instance has an unobserved law but contributes only one observation, so uncertainty persists even if the mixture law is known. We propose Conformal-DRO, which uses nested conformal regions to construct an ambiguity set for the future latent law. Under exchangeability, the set covers this law with probability at least $1-\alpha$ in finite samples, without estimating underlying latent laws or their mixing mechanism. The conformal path induces a data-driven transport geometry, while $\alpha$ determines the radius. The worst-case problem reduces to a finite linear program over conformal shells and admits sparse adversarial solutions. The resulting robust value provides a finite-sample certificate for the selected decision's expected cost.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in stat.ML).
Categorie arXiv: Optimization and Control (math.OC) ; Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.11073 — 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 2026-10-16T05:47:17.434Z dalla pagina https://arxiv.org/catchup/stat.ML/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).