{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:TA2CDE6WRY37FX275446BV3SLD","short_pith_number":"pith:TA2CDE6W","schema_version":"1.0","canonical_sha256":"98342193d68e37f2df5fef39e0d77258e4e5c08364713a33f8456b924cc1a066","source":{"kind":"arxiv","id":"1201.3571","version":1},"attestation_state":"computed","paper":{"title":"A Generic Path Algorithm for Regularized Statistical Estimation","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Hua Zhou, Yichao Wu","submitted_at":"2012-01-17T17:42:46Z","abstract_excerpt":"Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution towards prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized $l_1$ penalized regression methods. Although there has been a lot of research in this area, developing efficient optimization methods for ma"},"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":"1201.3571","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T17:42:46Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"3d4ead0593eb6b7643b9741eba12a53b1ceadf2155b606f641509604c96c2dd5","abstract_canon_sha256":"11faffd7675ab09c87fe6944f8f11c8ef8dd314aced4f8b5aa56bfed6e86890e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:28.901009Z","signature_b64":"JX9Ph9Bhi/QasFc8B9hHz25vq9op8bBEARlx/cXYrvwkyZ1kA96v3XHlI/TQm9fP/Z2zG+gbJ4+Fm7XuMQdgCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98342193d68e37f2df5fef39e0d77258e4e5c08364713a33f8456b924cc1a066","last_reissued_at":"2026-05-18T04:04:28.900192Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:28.900192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generic Path Algorithm for Regularized Statistical Estimation","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Hua Zhou, Yichao Wu","submitted_at":"2012-01-17T17:42:46Z","abstract_excerpt":"Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution towards prior information. In general, a regularized estimation problem minimizes the sum of a loss function and a penalty term. The penalty term is usually weighted by a tuning parameter and encourages certain constraints on the parameters to be estimated. Particular choices of constraints lead to the popular lasso, fused-lasso, and other generalized $l_1$ penalized regression methods. Although there has been a lot of research in this area, developing efficient optimization methods for ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.3571","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":"1201.3571","created_at":"2026-05-18T04:04:28.900323+00:00"},{"alias_kind":"arxiv_version","alias_value":"1201.3571v1","created_at":"2026-05-18T04:04:28.900323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.3571","created_at":"2026-05-18T04:04:28.900323+00:00"},{"alias_kind":"pith_short_12","alias_value":"TA2CDE6WRY37","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_16","alias_value":"TA2CDE6WRY37FX27","created_at":"2026-05-18T12:27:23.164592+00:00"},{"alias_kind":"pith_short_8","alias_value":"TA2CDE6W","created_at":"2026-05-18T12:27:23.164592+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/TA2CDE6WRY37FX275446BV3SLD","json":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD.json","graph_json":"https://pith.science/api/pith-number/TA2CDE6WRY37FX275446BV3SLD/graph.json","events_json":"https://pith.science/api/pith-number/TA2CDE6WRY37FX275446BV3SLD/events.json","paper":"https://pith.science/paper/TA2CDE6W"},"agent_actions":{"view_html":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD","download_json":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD.json","view_paper":"https://pith.science/paper/TA2CDE6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1201.3571&json=true","fetch_graph":"https://pith.science/api/pith-number/TA2CDE6WRY37FX275446BV3SLD/graph.json","fetch_events":"https://pith.science/api/pith-number/TA2CDE6WRY37FX275446BV3SLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD/action/storage_attestation","attest_author":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD/action/author_attestation","sign_citation":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD/action/citation_signature","submit_replication":"https://pith.science/pith/TA2CDE6WRY37FX275446BV3SLD/action/replication_record"}},"created_at":"2026-05-18T04:04:28.900323+00:00","updated_at":"2026-05-18T04:04:28.900323+00:00"}