{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ROQMLJFYEJ4LUC3IPSQHKUOTZ7","short_pith_number":"pith:ROQMLJFY","schema_version":"1.0","canonical_sha256":"8ba0c5a4b82278ba0b687ca07551d3cff8c78b55ab67e428392d3151bccc737f","source":{"kind":"arxiv","id":"1807.06981","version":1},"attestation_state":"computed","paper":{"title":"A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aur\\'elien Bellet, Robin Vogel, St\\'ephan Cl\\'emen\\c{c}on","submitted_at":"2018-07-18T14:47:54Z","abstract_excerpt":"The performance of many machine learning techniques depends on the choice of an appropriate similarity or distance measure on the input space. Similarity learning (or metric learning) aims at building such a measure from training data so that observations with the same (resp. different) label are as close (resp. far) as possible. In this paper, similarity learning is investigated from the perspective of pairwise bipartite ranking, where the goal is to rank the elements of a database by decreasing order of the probability that they share the same label with some query data point, based on the 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":"1807.06981","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-18T14:47:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"08103c85a1fc916aa5876aa819f399b31d6b1b6fe9787f24c178ee4ab9a5f9a4","abstract_canon_sha256":"a1e52272ffd32350aee213004588f0815bb2eac12deca16d01319279e091c37b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:38.491297Z","signature_b64":"2oJVAZhLBlJxn03EqS/xlukgOPxz097U71iCnhq/0nr0+wAJ2snwXdszLU/Hx8JQ1Bgaa+YucPiqk24Kl+hdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ba0c5a4b82278ba0b687ca07551d3cff8c78b55ab67e428392d3151bccc737f","last_reissued_at":"2026-05-17T23:55:38.490806Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:38.490806Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aur\\'elien Bellet, Robin Vogel, St\\'ephan Cl\\'emen\\c{c}on","submitted_at":"2018-07-18T14:47:54Z","abstract_excerpt":"The performance of many machine learning techniques depends on the choice of an appropriate similarity or distance measure on the input space. Similarity learning (or metric learning) aims at building such a measure from training data so that observations with the same (resp. different) label are as close (resp. far) as possible. In this paper, similarity learning is investigated from the perspective of pairwise bipartite ranking, where the goal is to rank the elements of a database by decreasing order of the probability that they share the same label with some query data point, based on the s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06981","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":"1807.06981","created_at":"2026-05-17T23:55:38.490891+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.06981v1","created_at":"2026-05-17T23:55:38.490891+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06981","created_at":"2026-05-17T23:55:38.490891+00:00"},{"alias_kind":"pith_short_12","alias_value":"ROQMLJFYEJ4L","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"ROQMLJFYEJ4LUC3I","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"ROQMLJFY","created_at":"2026-05-18T12:32:50.500415+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/ROQMLJFYEJ4LUC3IPSQHKUOTZ7","json":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7.json","graph_json":"https://pith.science/api/pith-number/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/graph.json","events_json":"https://pith.science/api/pith-number/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/events.json","paper":"https://pith.science/paper/ROQMLJFY"},"agent_actions":{"view_html":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7","download_json":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7.json","view_paper":"https://pith.science/paper/ROQMLJFY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.06981&json=true","fetch_graph":"https://pith.science/api/pith-number/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/graph.json","fetch_events":"https://pith.science/api/pith-number/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/action/storage_attestation","attest_author":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/action/author_attestation","sign_citation":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/action/citation_signature","submit_replication":"https://pith.science/pith/ROQMLJFYEJ4LUC3IPSQHKUOTZ7/action/replication_record"}},"created_at":"2026-05-17T23:55:38.490891+00:00","updated_at":"2026-05-17T23:55:38.490891+00:00"}