{"paper":{"title":"An inexact subsampled proximal Newton-type method for large-scale machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","stat.ML"],"primary_cat":"cs.LG","authors_text":"Cho-Jui Hsieh, Jason D. Lee, Xuanqing Liu, Yuekai Sun","submitted_at":"2017-08-28T22:47:48Z","abstract_excerpt":"We propose a fast proximal Newton-type algorithm for minimizing regularized finite sums that returns an $\\epsilon$-suboptimal point in $\\tilde{\\mathcal{O}}(d(n + \\sqrt{\\kappa d})\\log(\\frac{1}{\\epsilon}))$ FLOPS, where $n$ is number of samples, $d$ is feature dimension, and $\\kappa$ is the condition number. As long as $n > d$, the proposed method is more efficient than state-of-the-art accelerated stochastic first-order methods for non-smooth regularizers which requires $\\tilde{\\mathcal{O}}(d(n + \\sqrt{\\kappa n})\\log(\\frac{1}{\\epsilon}))$ FLOPS. The key idea is to form the subsampled Newton sub"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08552","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"}