{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:OH7MQUL7ZDBKIBIGVBUQZE7UKX","short_pith_number":"pith:OH7MQUL7","schema_version":"1.0","canonical_sha256":"71fec8517fc8c2a40506a8690c93f455f2ec944240a1d29b4ec2aaf1189c8678","source":{"kind":"arxiv","id":"1504.06637","version":1},"attestation_state":"computed","paper":{"title":"ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Eric Chi, Genevera I. Allen, Yue Hu","submitted_at":"2015-04-24T20:48:51Z","abstract_excerpt":"Optimization approaches based on operator splitting are becoming popular for solving sparsity regularized statistical machine learning models. While many have proposed fast algorithms to solve these problems for a single regularization parameter, conspicuously less attention has been given to computing regularization paths, or solving the optimization problems over the full range of regularization parameters to obtain a sequence of sparse models. In this chapter, we aim to quickly approximate the sequence of sparse models associated with regularization paths for the purposes of statistical mod"},"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":"1504.06637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-04-24T20:48:51Z","cross_cats_sorted":[],"title_canon_sha256":"7c00116019b9d50de7448c8912fa71c31c6a062678757fe21443c08c7196aad2","abstract_canon_sha256":"fbe4c0a9576c89eb7a4d58882b3052b5b60343e0682d2b7433b10ab7d66393f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:52.907159Z","signature_b64":"rWjyVmsViGtitR7epA8gY3DjVn8A7M+wfy1tJjfIgc3/JCABz/UGemhRRvbHlrZhyymWafPzuJwJi+lVGWU0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71fec8517fc8c2a40506a8690c93f455f2ec944240a1d29b4ec2aaf1189c8678","last_reissued_at":"2026-05-18T02:17:52.906641Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:52.906641Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Eric Chi, Genevera I. Allen, Yue Hu","submitted_at":"2015-04-24T20:48:51Z","abstract_excerpt":"Optimization approaches based on operator splitting are becoming popular for solving sparsity regularized statistical machine learning models. While many have proposed fast algorithms to solve these problems for a single regularization parameter, conspicuously less attention has been given to computing regularization paths, or solving the optimization problems over the full range of regularization parameters to obtain a sequence of sparse models. In this chapter, we aim to quickly approximate the sequence of sparse models associated with regularization paths for the purposes of statistical mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.06637","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1504.06637","created_at":"2026-05-18T02:17:52.906714+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.06637v1","created_at":"2026-05-18T02:17:52.906714+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.06637","created_at":"2026-05-18T02:17:52.906714+00:00"},{"alias_kind":"pith_short_12","alias_value":"OH7MQUL7ZDBK","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"OH7MQUL7ZDBKIBIG","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"OH7MQUL7","created_at":"2026-05-18T12:29:34.919912+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/OH7MQUL7ZDBKIBIGVBUQZE7UKX","json":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX.json","graph_json":"https://pith.science/api/pith-number/OH7MQUL7ZDBKIBIGVBUQZE7UKX/graph.json","events_json":"https://pith.science/api/pith-number/OH7MQUL7ZDBKIBIGVBUQZE7UKX/events.json","paper":"https://pith.science/paper/OH7MQUL7"},"agent_actions":{"view_html":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX","download_json":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX.json","view_paper":"https://pith.science/paper/OH7MQUL7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.06637&json=true","fetch_graph":"https://pith.science/api/pith-number/OH7MQUL7ZDBKIBIGVBUQZE7UKX/graph.json","fetch_events":"https://pith.science/api/pith-number/OH7MQUL7ZDBKIBIGVBUQZE7UKX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX/action/storage_attestation","attest_author":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX/action/author_attestation","sign_citation":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX/action/citation_signature","submit_replication":"https://pith.science/pith/OH7MQUL7ZDBKIBIGVBUQZE7UKX/action/replication_record"}},"created_at":"2026-05-18T02:17:52.906714+00:00","updated_at":"2026-05-18T02:17:52.906714+00:00"}