{"paper":{"title":"Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Gong-Duo Zhang, Ming-Wei Li, Shen-Yi Zhao, Wu-Jun Li","submitted_at":"2018-03-15T07:38:50Z","abstract_excerpt":"Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regularization. In this paper, we propose a novel method, called proximal \\mbox{SCOPE}~(\\mbox{pSCOPE}), for distributed sparse learning with $L_1$ regularization. pSCOPE is based on a \\underline{c}ooperative \\underline{a}utonomous \\underline{l}ocal \\underline{l}earning~(\\mbox{CALL}) framework. In the \\mbox{CALL} framework of \\mbox{pSCOPE}, we find that th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05621","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"}