{"paper":{"title":"On Learning for Ambiguous Chance Constrained Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"A Ch Madhusudanarao, Rahul Singh","submitted_at":"2023-12-31T17:25:43Z","abstract_excerpt":"We study chance constrained optimization problems $\\min_x f(x)$ s.t. $P(\\left\\{ \\theta: g(x,\\theta)\\le 0 \\right\\})\\ge 1-\\epsilon$ where $\\epsilon\\in (0,1)$ is the violation probability, when the distribution $P$ is not known to the decision maker (DM). When the DM has access to a set of distributions $\\mathcal{U}$ such that $P$ is contained in $\\mathcal{U}$, then the problem is known as the ambiguous chance-constrained problem \\cite{erdougan2006ambiguous}. We study ambiguous chance-constrained problem for the case when $\\mathcal{U}$ is of the form $\\left\\{\\mu:\\frac{\\mu (y)}{\\nu(y)}\\leq C, \\for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.00547","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.00547/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}