{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZGSXA55PA7IZNXVG7JA23TSB2V","short_pith_number":"pith:ZGSXA55P","schema_version":"1.0","canonical_sha256":"c9a57077af07d196dea6fa41adce41d543b9b0a286cf26d9959af5aad638b084","source":{"kind":"arxiv","id":"1803.00783","version":1},"attestation_state":"computed","paper":{"title":"Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa","submitted_at":"2018-03-02T09:50:11Z","abstract_excerpt":"In statistical machine learning, kernel methods allow to consider infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done by solving an optimization problem depending on a data fit term and a suitable regularizer. In this paper we consider feature maps which are the concatenation of a fixed, possibly large, set of simpler feature maps. The penalty is a sparsity inducing one, promoting solutions depending only on a small subset of the features. The group lasso problem is a special case of this more general setting. We 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":"1803.00783","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-03-02T09:50:11Z","cross_cats_sorted":[],"title_canon_sha256":"359792ee8735cf1521aa1a4b58b0a12af91c65c425aa61c7af7acc1bfd5a5c9c","abstract_canon_sha256":"280e7a0d646c2a6e05e0686e50b8a32cf18d76b428b8d054ee6175038c04e4c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:15.322716Z","signature_b64":"4a6GX/No9m7gcBYxq7BR7F4til6nPyWr2QqrrdDI7AGOzZBzvWJucg2RYU9I8x6GJd/bNy7D2Mg3NRKWyAokAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9a57077af07d196dea6fa41adce41d543b9b0a286cf26d9959af5aad638b084","last_reissued_at":"2026-05-17T23:56:15.322159Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:15.322159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Multiple Kernel Learning: Support Identification via Mirror Stratifiability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa","submitted_at":"2018-03-02T09:50:11Z","abstract_excerpt":"In statistical machine learning, kernel methods allow to consider infinite dimensional feature spaces with a computational cost that only depends on the number of observations. This is usually done by solving an optimization problem depending on a data fit term and a suitable regularizer. In this paper we consider feature maps which are the concatenation of a fixed, possibly large, set of simpler feature maps. The penalty is a sparsity inducing one, promoting solutions depending only on a small subset of the features. The group lasso problem is a special case of this more general setting. We s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00783","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":"1803.00783","created_at":"2026-05-17T23:56:15.322250+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.00783v1","created_at":"2026-05-17T23:56:15.322250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00783","created_at":"2026-05-17T23:56:15.322250+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGSXA55PA7IZ","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGSXA55PA7IZNXVG","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGSXA55P","created_at":"2026-05-18T12:33:07.085635+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/ZGSXA55PA7IZNXVG7JA23TSB2V","json":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V.json","graph_json":"https://pith.science/api/pith-number/ZGSXA55PA7IZNXVG7JA23TSB2V/graph.json","events_json":"https://pith.science/api/pith-number/ZGSXA55PA7IZNXVG7JA23TSB2V/events.json","paper":"https://pith.science/paper/ZGSXA55P"},"agent_actions":{"view_html":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V","download_json":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V.json","view_paper":"https://pith.science/paper/ZGSXA55P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.00783&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGSXA55PA7IZNXVG7JA23TSB2V/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGSXA55PA7IZNXVG7JA23TSB2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V/action/storage_attestation","attest_author":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V/action/author_attestation","sign_citation":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V/action/citation_signature","submit_replication":"https://pith.science/pith/ZGSXA55PA7IZNXVG7JA23TSB2V/action/replication_record"}},"created_at":"2026-05-17T23:56:15.322250+00:00","updated_at":"2026-05-17T23:56:15.322250+00:00"}