{"paper":{"title":"Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Arian Maleki, Haihao Lu, Shuaiwen Wang, Vahab Mirrokni, Wenda Zhou","submitted_at":"2018-07-07T17:00:24Z","abstract_excerpt":"Consider the following class of learning schemes: $$\\hat{\\boldsymbol{\\beta}} := \\arg\\min_{\\boldsymbol{\\beta}}\\;\\sum_{j=1}^n \\ell(\\boldsymbol{x}_j^\\top\\boldsymbol{\\beta}; y_j) + \\lambda R(\\boldsymbol{\\beta}),\\qquad\\qquad (1) $$ where $\\boldsymbol{x}_i \\in \\mathbb{R}^p$ and $y_i \\in \\mathbb{R}$ denote the $i^{\\text{th}}$ feature and response variable respectively. Let $\\ell$ and $R$ be the loss function and regularizer, $\\boldsymbol{\\beta}$ denote the unknown weights, and $\\lambda$ be a regularization parameter. Finding the optimal choice of $\\lambda$ is a challenging problem in high-dimensional"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02694","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"}