{"paper":{"title":"Contextual Search via Intrinsic Volumes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.MG"],"primary_cat":"cs.DS","authors_text":"Jon Schneider, Renato Paes Leme","submitted_at":"2018-04-09T19:30:29Z","abstract_excerpt":"We study the problem of contextual search, a multidimensional generalization of binary search that captures many problems in contextual decision-making. In contextual search, a learner is trying to learn the value of a hidden vector $v \\in [0,1]^d$. Every round the learner is provided an adversarially-chosen context $u_t \\in \\mathbb{R}^d$, submits a guess $p_t$ for the value of $\\langle u_t, v\\rangle$, learns whether $p_t < \\langle u_t, v\\rangle$, and incurs loss $\\ell(\\langle u_t, v\\rangle, p_t)$ (for some loss function $\\ell$). The learner's goal is to minimize their total loss over the cour"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03195","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":""},"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"}