{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HOSHZ3RZS6FA3HQUFKBP4BWCED","short_pith_number":"pith:HOSHZ3RZ","schema_version":"1.0","canonical_sha256":"3ba47cee39978a0d9e142a82fe06c220eb035de2341d52b1cf0eb7c4ea99fcbd","source":{"kind":"arxiv","id":"1806.10701","version":2},"attestation_state":"computed","paper":{"title":"Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"David M. Blei, Morgane Austern, Peter Orbanz, Victor Veitch, Wenda Zhou","submitted_at":"2018-06-27T22:08:54Z","abstract_excerpt":"Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased. This is achieved by considering the method by which data is sampled from a graph as an explicit component of model design. By integrating fast implementations of graph sampling schemes with standard automatic differentiation tools, we provide an efficient turnk"},"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":"1806.10701","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-27T22:08:54Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"60b09f1644971ab9a69bb5923980e5ae299deb5636cdca0e02c14b491aba9e57","abstract_canon_sha256":"3a868c811694be386b91f6a06f883996204e3c19ec9e63d60c5e778e877448a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:59.063987Z","signature_b64":"yM/hAfXZRBVwCS0vXKKrQ3Uudd8OfcxjCoAcWGIGeYrj7QBKnhTaiur6RqrZqT8cNgM/NB/2rlJekkoorivwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ba47cee39978a0d9e142a82fe06c220eb035de2341d52b1cf0eb7c4ea99fcbd","last_reissued_at":"2026-05-17T23:52:59.063288Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:59.063288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"David M. Blei, Morgane Austern, Peter Orbanz, Victor Veitch, Wenda Zhou","submitted_at":"2018-06-27T22:08:54Z","abstract_excerpt":"Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased. This is achieved by considering the method by which data is sampled from a graph as an explicit component of model design. By integrating fast implementations of graph sampling schemes with standard automatic differentiation tools, we provide an efficient turnk"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10701","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":"1806.10701","created_at":"2026-05-17T23:52:59.063410+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.10701v2","created_at":"2026-05-17T23:52:59.063410+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10701","created_at":"2026-05-17T23:52:59.063410+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOSHZ3RZS6FA","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOSHZ3RZS6FA3HQU","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOSHZ3RZ","created_at":"2026-05-18T12:32:28.185984+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/HOSHZ3RZS6FA3HQUFKBP4BWCED","json":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED.json","graph_json":"https://pith.science/api/pith-number/HOSHZ3RZS6FA3HQUFKBP4BWCED/graph.json","events_json":"https://pith.science/api/pith-number/HOSHZ3RZS6FA3HQUFKBP4BWCED/events.json","paper":"https://pith.science/paper/HOSHZ3RZ"},"agent_actions":{"view_html":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED","download_json":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED.json","view_paper":"https://pith.science/paper/HOSHZ3RZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.10701&json=true","fetch_graph":"https://pith.science/api/pith-number/HOSHZ3RZS6FA3HQUFKBP4BWCED/graph.json","fetch_events":"https://pith.science/api/pith-number/HOSHZ3RZS6FA3HQUFKBP4BWCED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED/action/storage_attestation","attest_author":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED/action/author_attestation","sign_citation":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED/action/citation_signature","submit_replication":"https://pith.science/pith/HOSHZ3RZS6FA3HQUFKBP4BWCED/action/replication_record"}},"created_at":"2026-05-17T23:52:59.063410+00:00","updated_at":"2026-05-17T23:52:59.063410+00:00"}