Metraipolis › archive › dc_3433d2fc4232

CAP: Learning to Optimize from Sparse Observations of Related Tasks — arXiv:2610.41173

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41173
License (as archived): CC0-1.0
Archived: 2026-11-02T07:49:26.421Z
SHA-256 of the archived text: dd02a5ad9d2491b53d60d7c02c3eb456265b71406f941d6fb7fd81300d08aa3b
Stable URL of this page: https://metraipolis.com/documento/dc_3433d2fc4232

How to cite

CAP: Learning to Optimize from Sparse Observations of Related Tasks — arXiv:2610.41173 — arXiv (en), https://arxiv.org/abs/2610.41173 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3433d2fc4232 (Metraipolis archive dc_3433d2fc4232, sha256 dd02a5ad9d2491b53d60d7c02c3eb456265b71406f941d6fb7fd81300d08aa3b)

BibTeX

@misc{dc3433d2fc4232,
  title = {CAP: Learning to Optimize from Sparse Observations of Related Tasks — arXiv:2610.41173},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41173},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3433d2fc4232; sha256 dd02a5ad9d2491b53d60d7c02c3eb456265b71406f941d6fb7fd81300d08aa3b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41173. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In experiment-driven black-box optimization, each evaluation can consume materials, equipment time, and other resources, limiting the number of experiments available for a new task. Records from completed related optimization campaigns could help, but learning a sequential query policy remains challenging when task-specific experience consists of only a few sparse histories. We propose CAP (Candidate Acquisition Policy), which models shared structure and task-specific variation in fixed historical records to sample interactive practice functions. On these functions, CAP learns a feedback-conditioned query policy aimed at final optimization performance under a fixed evaluation budget. At deployment, its parameters remain fixed while each new observation informs the next query. Across five synthetic task families spanning 6 and 20 dimensions and two engineering simulation benchmarks, BOPTEST and SU2, CAP attains the lowest median final-budget value among the reported methods on four synthetic families and both engineering benchmarks. These results indicate that sparse records from completed related tasks can support adaptive query decisions on new tasks, making more effective use of limited, costly experimental evaluations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.41173 — 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 2026-11-02T07:40:00.175Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).