{"paper":{"title":"Signed Support Recovery for Single Index Models in High-Dimensions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Jun S. Liu, Matey Neykov, Qian Lin","submitted_at":"2015-11-07T00:23:53Z","abstract_excerpt":"In this paper we study the support recovery problem for single index models $Y=f(\\boldsymbol{X}^{\\intercal} \\boldsymbol{\\beta},\\varepsilon)$, where $f$ is an unknown link function, $\\boldsymbol{X}\\sim N_p(0,\\mathbb{I}_{p})$ and $\\boldsymbol{\\beta}$ is an $s$-sparse unit vector such that $\\boldsymbol{\\beta}_{i}\\in \\{\\pm\\frac{1}{\\sqrt{s}},0\\}$. In particular, we look into the performance of two computationally inexpensive algorithms: (a) the diagonal thresholding sliced inverse regression (DT-SIR) introduced by Lin et al. (2015); and (b) a semi-definite programming (SDP) approach inspired by Ami"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02270","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"}