{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:GB6RRQXC7X7RZDJDQPLBDUDU47","short_pith_number":"pith:GB6RRQXC","schema_version":"1.0","canonical_sha256":"307d18c2e2fdff1c8d2383d611d074e7d5711dae43fe4916cafbb12123f3303a","source":{"kind":"arxiv","id":"1410.0247","version":2},"attestation_state":"computed","paper":{"title":"A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Johannes Lederer, Martin Wainwright, Micha\\\"el Chichignoud","submitted_at":"2014-10-01T14:55:34Z","abstract_excerpt":"We introduce a novel scheme for choosing the regularization parameter in high-dimensional linear regression with Lasso. This scheme, inspired by Lepski's method for bandwidth selection in non-parametric regression, is equipped with both optimal finite-sample guarantees and a fast algorithm. In particular, for any design matrix such that the Lasso has low sup-norm error under an \"oracle choice\" of the regularization parameter, we show that our method matches the oracle performance up to a small constant factor, and show that it can be implemented by performing simple tests along a single Lasso "},"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":"1410.0247","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-01T14:55:34Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"a071463cce9aaa722261a43d1304f1e746cedf371a9584a18f28bfe90f323b26","abstract_canon_sha256":"645f42b73202b5cf2e1a995453aa94c8cc77a3d5d71066908d71865d0cc4e682"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:59:59.198136Z","signature_b64":"gZHyRcO87bvZoLNPD+YyeSaEgKjF4YOdEav44idky2IEV3RMY2swvbIil0gCk7spEy7Spr3HIDY6QtjT7k9YAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"307d18c2e2fdff1c8d2383d611d074e7d5711dae43fe4916cafbb12123f3303a","last_reissued_at":"2026-05-18T00:59:59.197533Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:59:59.197533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Johannes Lederer, Martin Wainwright, Micha\\\"el Chichignoud","submitted_at":"2014-10-01T14:55:34Z","abstract_excerpt":"We introduce a novel scheme for choosing the regularization parameter in high-dimensional linear regression with Lasso. This scheme, inspired by Lepski's method for bandwidth selection in non-parametric regression, is equipped with both optimal finite-sample guarantees and a fast algorithm. In particular, for any design matrix such that the Lasso has low sup-norm error under an \"oracle choice\" of the regularization parameter, we show that our method matches the oracle performance up to a small constant factor, and show that it can be implemented by performing simple tests along a single Lasso "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.0247","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":""},"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":"1410.0247","created_at":"2026-05-18T00:59:59.197628+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.0247v2","created_at":"2026-05-18T00:59:59.197628+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.0247","created_at":"2026-05-18T00:59:59.197628+00:00"},{"alias_kind":"pith_short_12","alias_value":"GB6RRQXC7X7R","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_16","alias_value":"GB6RRQXC7X7RZDJD","created_at":"2026-05-18T12:28:30.664211+00:00"},{"alias_kind":"pith_short_8","alias_value":"GB6RRQXC","created_at":"2026-05-18T12:28:30.664211+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/GB6RRQXC7X7RZDJDQPLBDUDU47","json":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47.json","graph_json":"https://pith.science/api/pith-number/GB6RRQXC7X7RZDJDQPLBDUDU47/graph.json","events_json":"https://pith.science/api/pith-number/GB6RRQXC7X7RZDJDQPLBDUDU47/events.json","paper":"https://pith.science/paper/GB6RRQXC"},"agent_actions":{"view_html":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47","download_json":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47.json","view_paper":"https://pith.science/paper/GB6RRQXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.0247&json=true","fetch_graph":"https://pith.science/api/pith-number/GB6RRQXC7X7RZDJDQPLBDUDU47/graph.json","fetch_events":"https://pith.science/api/pith-number/GB6RRQXC7X7RZDJDQPLBDUDU47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47/action/storage_attestation","attest_author":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47/action/author_attestation","sign_citation":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47/action/citation_signature","submit_replication":"https://pith.science/pith/GB6RRQXC7X7RZDJDQPLBDUDU47/action/replication_record"}},"created_at":"2026-05-18T00:59:59.197628+00:00","updated_at":"2026-05-18T00:59:59.197628+00:00"}