{"paper":{"title":"The All-or-Nothing Phenomenon in Sparse Linear Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"Galen Reeves, Ilias Zadik, Jiaming Xu","submitted_at":"2019-03-12T16:53:27Z","abstract_excerpt":"We study the problem of recovering a hidden binary $k$-sparse $p$-dimensional vector $\\beta$ from $n$ noisy linear observations $Y=X\\beta+W$ where $X_{ij}$ are i.i.d. $\\mathcal{N}(0,1)$ and $W_i$ are i.i.d. $\\mathcal{N}(0,\\sigma^2)$. A closely related hypothesis testing problem is to distinguish the pair $(X,Y)$ generated from this structured model from a corresponding null model where $(X,Y)$ consist of purely independent Gaussian entries. In the low sparsity $k=o(p)$ and high signal to noise ratio $k/\\sigma^2=\\Omega\\left(1\\right)$ regime, we establish an `All-or-Nothing' information-theoreti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05046","kind":"arxiv","version":1},"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"}