{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WPFM3U5N4CFFMRBALKLUZ5IDN5","short_pith_number":"pith:WPFM3U5N","schema_version":"1.0","canonical_sha256":"b3cacdd3ade08a5644205a974cf5036f6876011291d5c97606b1514f5a200a9f","source":{"kind":"arxiv","id":"1805.01870","version":1},"attestation_state":"computed","paper":{"title":"Hedging parameter selection for basis pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alex Gibberd, Sandipan Roy, Stephane Chretien","submitted_at":"2018-05-04T17:33:27Z","abstract_excerpt":"In Compressed Sensing and high dimensional estimation, signal recovery often relies on sparsity assumptions and estimation is performed via $\\ell_1$-penalized least-squares optimization, a.k.a. LASSO. The $\\ell_1$ penalisation is usually controlled by a weight, also called \"relaxation parameter\", denoted by $\\lambda$. It is commonly thought that the practical efficiency of the LASSO for prediction crucially relies on accurate selection of $\\lambda$. In this short note, we propose to consider the hyper-parameter selection problem from a new perspective which combines the Hedge online learning m"},"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":"1805.01870","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2018-05-04T17:33:27Z","cross_cats_sorted":[],"title_canon_sha256":"f0992d6eb662f9d5be161a03d7a5ed7052343878c917dcd3dc3ebf2f51caa0a0","abstract_canon_sha256":"cd28d6c5b4495155e38d20812d345af12646d0ce02de9149cea59edf5c98ca93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:46.403519Z","signature_b64":"5vGD2si0f9T9iZnLAbfBDi8AzxxlS11ea+Sy2JjXfY+0o336VPp99HJtVFvnfdVOaysIAjbL2OP4VSvuWRitBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3cacdd3ade08a5644205a974cf5036f6876011291d5c97606b1514f5a200a9f","last_reissued_at":"2026-05-18T00:16:46.403015Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:46.403015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hedging parameter selection for basis pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alex Gibberd, Sandipan Roy, Stephane Chretien","submitted_at":"2018-05-04T17:33:27Z","abstract_excerpt":"In Compressed Sensing and high dimensional estimation, signal recovery often relies on sparsity assumptions and estimation is performed via $\\ell_1$-penalized least-squares optimization, a.k.a. LASSO. The $\\ell_1$ penalisation is usually controlled by a weight, also called \"relaxation parameter\", denoted by $\\lambda$. It is commonly thought that the practical efficiency of the LASSO for prediction crucially relies on accurate selection of $\\lambda$. In this short note, we propose to consider the hyper-parameter selection problem from a new perspective which combines the Hedge online learning m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.01870","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":"1805.01870","created_at":"2026-05-18T00:16:46.403093+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.01870v1","created_at":"2026-05-18T00:16:46.403093+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.01870","created_at":"2026-05-18T00:16:46.403093+00:00"},{"alias_kind":"pith_short_12","alias_value":"WPFM3U5N4CFF","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WPFM3U5N4CFFMRBA","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WPFM3U5N","created_at":"2026-05-18T12:33:01.666342+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/WPFM3U5N4CFFMRBALKLUZ5IDN5","json":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5.json","graph_json":"https://pith.science/api/pith-number/WPFM3U5N4CFFMRBALKLUZ5IDN5/graph.json","events_json":"https://pith.science/api/pith-number/WPFM3U5N4CFFMRBALKLUZ5IDN5/events.json","paper":"https://pith.science/paper/WPFM3U5N"},"agent_actions":{"view_html":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5","download_json":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5.json","view_paper":"https://pith.science/paper/WPFM3U5N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.01870&json=true","fetch_graph":"https://pith.science/api/pith-number/WPFM3U5N4CFFMRBALKLUZ5IDN5/graph.json","fetch_events":"https://pith.science/api/pith-number/WPFM3U5N4CFFMRBALKLUZ5IDN5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5/action/storage_attestation","attest_author":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5/action/author_attestation","sign_citation":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5/action/citation_signature","submit_replication":"https://pith.science/pith/WPFM3U5N4CFFMRBALKLUZ5IDN5/action/replication_record"}},"created_at":"2026-05-18T00:16:46.403093+00:00","updated_at":"2026-05-18T00:16:46.403093+00:00"}