{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FCCY7D3NC3WNETCHCBWJ7VKXVT","short_pith_number":"pith:FCCY7D3N","schema_version":"1.0","canonical_sha256":"28858f8f6d16ecd24c47106c9fd557acd345c8361af00477f79c9e4370758034","source":{"kind":"arxiv","id":"2108.10284","version":2},"attestation_state":"computed","paper":{"title":"Exclusive Group Lasso for Structured Variable Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Carlos Buelga, David Gregoratti, Xavier Mestre","submitted_at":"2021-08-23T16:55:13Z","abstract_excerpt":"A structured variable selection problem is considered in which the covariates, divided into predefined groups, activate according to sparse patterns with few nonzero entries per group. Capitalizing on the concept of atomic norm, a composite norm can be properly designed to promote such exclusive group sparsity patterns. The resulting norm lends itself to efficient and flexible regularized optimization algorithms for support recovery, like the proximal algorithm. Moreover, an active set algorithm is proposed that builds the solution by successively including structure atoms into the estimated s"},"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":"2108.10284","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-23T16:55:13Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"e61be006369ddf97a1c36646331c07b046a5d9f7d3cf7bde536bd26f85d85441","abstract_canon_sha256":"5fc04cf45c6bf947e8fdd56772aff5df1a508cd22237f07ce75547cd90b3b0e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:02.943692Z","signature_b64":"rbCm+UCJYDqgjgFJhJfE8zqVO9SOrvoUgi/30VCLvvjWefXEc/fqEgWp25OdpPQOc+tcMnHGFRS2hRCJKE21Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28858f8f6d16ecd24c47106c9fd557acd345c8361af00477f79c9e4370758034","last_reissued_at":"2026-07-05T07:08:02.943133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:02.943133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exclusive Group Lasso for Structured Variable Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Carlos Buelga, David Gregoratti, Xavier Mestre","submitted_at":"2021-08-23T16:55:13Z","abstract_excerpt":"A structured variable selection problem is considered in which the covariates, divided into predefined groups, activate according to sparse patterns with few nonzero entries per group. Capitalizing on the concept of atomic norm, a composite norm can be properly designed to promote such exclusive group sparsity patterns. The resulting norm lends itself to efficient and flexible regularized optimization algorithms for support recovery, like the proximal algorithm. Moreover, an active set algorithm is proposed that builds the solution by successively including structure atoms into the estimated s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.10284","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.10284/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2108.10284","created_at":"2026-07-05T07:08:02.943189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.10284v2","created_at":"2026-07-05T07:08:02.943189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.10284","created_at":"2026-07-05T07:08:02.943189+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCCY7D3NC3WN","created_at":"2026-07-05T07:08:02.943189+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCCY7D3NC3WNETCH","created_at":"2026-07-05T07:08:02.943189+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCCY7D3N","created_at":"2026-07-05T07:08:02.943189+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/FCCY7D3NC3WNETCHCBWJ7VKXVT","json":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT.json","graph_json":"https://pith.science/api/pith-number/FCCY7D3NC3WNETCHCBWJ7VKXVT/graph.json","events_json":"https://pith.science/api/pith-number/FCCY7D3NC3WNETCHCBWJ7VKXVT/events.json","paper":"https://pith.science/paper/FCCY7D3N"},"agent_actions":{"view_html":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT","download_json":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT.json","view_paper":"https://pith.science/paper/FCCY7D3N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.10284&json=true","fetch_graph":"https://pith.science/api/pith-number/FCCY7D3NC3WNETCHCBWJ7VKXVT/graph.json","fetch_events":"https://pith.science/api/pith-number/FCCY7D3NC3WNETCHCBWJ7VKXVT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT/action/storage_attestation","attest_author":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT/action/author_attestation","sign_citation":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT/action/citation_signature","submit_replication":"https://pith.science/pith/FCCY7D3NC3WNETCHCBWJ7VKXVT/action/replication_record"}},"created_at":"2026-07-05T07:08:02.943189+00:00","updated_at":"2026-07-05T07:08:02.943189+00:00"}