{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:WBQEVKIQNN7CLJKQN2DKT37R3G","short_pith_number":"pith:WBQEVKIQ","schema_version":"1.0","canonical_sha256":"b0604aa9106b7e25a5506e86a9eff1d981bcfaad2c763d562b598c2e94f1b43c","source":{"kind":"arxiv","id":"1708.08826","version":2},"attestation_state":"computed","paper":{"title":"Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Jarvis Haupt, Jeffrey Druce, Mojtaba Kadkhodaie Elyaderani, Stefano Gonella, Swayambhoo Jain","submitted_at":"2017-08-29T15:34:22Z","abstract_excerpt":"This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \\emph{group-sparse} structure in a {known,} fixed dictionary. We consider signals generated according to a natural probabilistic model, and establish new conditions under which the set of indices of the non-zero groups of the signal (called the group-level support) may be accurately estimated via the group Lasso. Our results strengthen existing coherence-based analyses that exhibit the well-known \"square root\" bottleneck, allowing for the n"},"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":"1708.08826","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-08-29T15:34:22Z","cross_cats_sorted":["math.IT","stat.ML"],"title_canon_sha256":"c5acc4e5664f6759f611450e5bc09da90bf02cc7eb343252b2bef284a2a1bce2","abstract_canon_sha256":"37da7419ff4d4415ee2c53bcd4604d603fc37f0802ceaf939b3e654e3f304722"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:37.433607Z","signature_b64":"Yzml8OvteBJ6AT6oqjp+qPLBG9OIHdpMbFDPfeuQnob+sG61nwyxYRrgyWZNodzj2t95NGbUhXlvd+AtJIafDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0604aa9106b7e25a5506e86a9eff1d981bcfaad2c763d562b598c2e94f1b43c","last_reissued_at":"2026-05-18T00:15:37.433006Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:37.433006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Jarvis Haupt, Jeffrey Druce, Mojtaba Kadkhodaie Elyaderani, Stefano Gonella, Swayambhoo Jain","submitted_at":"2017-08-29T15:34:22Z","abstract_excerpt":"This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \\emph{group-sparse} structure in a {known,} fixed dictionary. We consider signals generated according to a natural probabilistic model, and establish new conditions under which the set of indices of the non-zero groups of the signal (called the group-level support) may be accurately estimated via the group Lasso. Our results strengthen existing coherence-based analyses that exhibit the well-known \"square root\" bottleneck, allowing for the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08826","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":"1708.08826","created_at":"2026-05-18T00:15:37.433100+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.08826v2","created_at":"2026-05-18T00:15:37.433100+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08826","created_at":"2026-05-18T00:15:37.433100+00:00"},{"alias_kind":"pith_short_12","alias_value":"WBQEVKIQNN7C","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"WBQEVKIQNN7CLJKQ","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"WBQEVKIQ","created_at":"2026-05-18T12:31:53.515858+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/WBQEVKIQNN7CLJKQN2DKT37R3G","json":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G.json","graph_json":"https://pith.science/api/pith-number/WBQEVKIQNN7CLJKQN2DKT37R3G/graph.json","events_json":"https://pith.science/api/pith-number/WBQEVKIQNN7CLJKQN2DKT37R3G/events.json","paper":"https://pith.science/paper/WBQEVKIQ"},"agent_actions":{"view_html":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G","download_json":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G.json","view_paper":"https://pith.science/paper/WBQEVKIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.08826&json=true","fetch_graph":"https://pith.science/api/pith-number/WBQEVKIQNN7CLJKQN2DKT37R3G/graph.json","fetch_events":"https://pith.science/api/pith-number/WBQEVKIQNN7CLJKQN2DKT37R3G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G/action/storage_attestation","attest_author":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G/action/author_attestation","sign_citation":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G/action/citation_signature","submit_replication":"https://pith.science/pith/WBQEVKIQNN7CLJKQN2DKT37R3G/action/replication_record"}},"created_at":"2026-05-18T00:15:37.433100+00:00","updated_at":"2026-05-18T00:15:37.433100+00:00"}