{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:Y3TPZ4AULH22Z6BCLQYXH3IAS7","short_pith_number":"pith:Y3TPZ4AU","schema_version":"1.0","canonical_sha256":"c6e6fcf01459f5acf8225c3173ed0097ff8a4a5bc1fd733b8c65b5fe8718c99d","source":{"kind":"arxiv","id":"2004.03639","version":2},"attestation_state":"computed","paper":{"title":"Orthant Based Proximal Stochastic Gradient Method for $\\ell_1$-Regularized Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Bo Ji, Guanyi Wang, Jing Tian, Sheng Yi, Tianyi Chen, Tianyu Ding, Xiao Tu, Yixin Shi, Zhihui Zhu","submitted_at":"2020-04-07T18:23:39Z","abstract_excerpt":"Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel stochastic method -- Orthant Based Proximal Stochastic Gradient Method (OBProx-SG) -- to solve perhaps the most popular instance, i.e., the l1-regularized problem. The OBProx-SG method contains two steps: (i) a proximal stochastic gradient step to predict a support cover of the solution; and (ii) an orthant step to aggressively enhance the sparsity level via orthant face projection. Compared to the state-of-the-art met"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2004.03639","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2020-04-07T18:23:39Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5a8521ba41fa7d27262174d1ef4691e51b105841d84c69c90212e109b9d044f6","abstract_canon_sha256":"5ee92450a2511d52fe3730e6416d53f1143a40496561d7d7d32cd67b74e53f4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:21:36.798058Z","signature_b64":"Y2PKVBUMDh2h5+VlvA1qVBbdmMYXVpMpTq5N9vGMsQ+Kqjc56U0ePgnVk45/eQC3zKAdCj6UbHKJH6/uczL3Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6e6fcf01459f5acf8225c3173ed0097ff8a4a5bc1fd733b8c65b5fe8718c99d","last_reissued_at":"2026-07-05T01:21:36.797663Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:21:36.797663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Orthant Based Proximal Stochastic Gradient Method for $\\ell_1$-Regularized Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Bo Ji, Guanyi Wang, Jing Tian, Sheng Yi, Tianyi Chen, Tianyu Ding, Xiao Tu, Yixin Shi, Zhihui Zhu","submitted_at":"2020-04-07T18:23:39Z","abstract_excerpt":"Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression. In this paper, we present a novel stochastic method -- Orthant Based Proximal Stochastic Gradient Method (OBProx-SG) -- to solve perhaps the most popular instance, i.e., the l1-regularized problem. The OBProx-SG method contains two steps: (i) a proximal stochastic gradient step to predict a support cover of the solution; and (ii) an orthant step to aggressively enhance the sparsity level via orthant face projection. Compared to the state-of-the-art met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.03639","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2004.03639/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2004.03639","created_at":"2026-07-05T01:21:36.797714+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.03639v2","created_at":"2026-07-05T01:21:36.797714+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.03639","created_at":"2026-07-05T01:21:36.797714+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y3TPZ4AULH22","created_at":"2026-07-05T01:21:36.797714+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y3TPZ4AULH22Z6BC","created_at":"2026-07-05T01:21:36.797714+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y3TPZ4AU","created_at":"2026-07-05T01:21:36.797714+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7","json":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7.json","graph_json":"https://pith.science/api/pith-number/Y3TPZ4AULH22Z6BCLQYXH3IAS7/graph.json","events_json":"https://pith.science/api/pith-number/Y3TPZ4AULH22Z6BCLQYXH3IAS7/events.json","paper":"https://pith.science/paper/Y3TPZ4AU"},"agent_actions":{"view_html":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7","download_json":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7.json","view_paper":"https://pith.science/paper/Y3TPZ4AU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.03639&json=true","fetch_graph":"https://pith.science/api/pith-number/Y3TPZ4AULH22Z6BCLQYXH3IAS7/graph.json","fetch_events":"https://pith.science/api/pith-number/Y3TPZ4AULH22Z6BCLQYXH3IAS7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7/action/storage_attestation","attest_author":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7/action/author_attestation","sign_citation":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7/action/citation_signature","submit_replication":"https://pith.science/pith/Y3TPZ4AULH22Z6BCLQYXH3IAS7/action/replication_record"}},"created_at":"2026-07-05T01:21:36.797714+00:00","updated_at":"2026-07-05T01:21:36.797714+00:00"}