{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:D3N3L53BHRWQ32BYPIUW4QMQCW","short_pith_number":"pith:D3N3L53B","schema_version":"1.0","canonical_sha256":"1edbb5f7613c6d0de8387a296e419015b97079448f5462e135537009192df98f","source":{"kind":"arxiv","id":"1310.7780","version":2},"attestation_state":"computed","paper":{"title":"The Information Geometry of Mirror Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Garvesh Raskutti, Sayan Mukherjee","submitted_at":"2013-10-29T12:21:12Z","abstract_excerpt":"Information geometry applies concepts in differential geometry to probability and statistics and is especially useful for parameter estimation in exponential families where parameters are known to lie on a Riemannian manifold. Connections between the geometric properties of the induced manifold and statistical properties of the estimation problem are well-established. However developing first-order methods that scale to larger problems has been less of a focus in the information geometry community. The best known algorithm that incorporates manifold structure is the second-order natural gradie"},"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":"1310.7780","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-10-29T12:21:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bd6afd1751d11010116c73531252a104c4dc31274a5195ebe7252b07a58e0107","abstract_canon_sha256":"fca40746fb11d40a25b89a336020e6e3d3d483a793b0688d84b3078268278ec7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:55.939962Z","signature_b64":"0u+jLwIt6XVRJ1Eaxfj7t8jvlhfRp77+iV6zMNwoMfIuiIdycu+/1H+Y5Xvr5i6tKQGOfTMnzHd/1lEGSqCVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1edbb5f7613c6d0de8387a296e419015b97079448f5462e135537009192df98f","last_reissued_at":"2026-05-18T02:52:55.939399Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:55.939399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Information Geometry of Mirror Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Garvesh Raskutti, Sayan Mukherjee","submitted_at":"2013-10-29T12:21:12Z","abstract_excerpt":"Information geometry applies concepts in differential geometry to probability and statistics and is especially useful for parameter estimation in exponential families where parameters are known to lie on a Riemannian manifold. Connections between the geometric properties of the induced manifold and statistical properties of the estimation problem are well-established. However developing first-order methods that scale to larger problems has been less of a focus in the information geometry community. The best known algorithm that incorporates manifold structure is the second-order natural gradie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.7780","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":"1310.7780","created_at":"2026-05-18T02:52:55.939496+00:00"},{"alias_kind":"arxiv_version","alias_value":"1310.7780v2","created_at":"2026-05-18T02:52:55.939496+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.7780","created_at":"2026-05-18T02:52:55.939496+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3N3L53BHRWQ","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3N3L53BHRWQ32BY","created_at":"2026-05-18T12:27:40.988391+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3N3L53B","created_at":"2026-05-18T12:27:40.988391+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/D3N3L53BHRWQ32BYPIUW4QMQCW","json":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW.json","graph_json":"https://pith.science/api/pith-number/D3N3L53BHRWQ32BYPIUW4QMQCW/graph.json","events_json":"https://pith.science/api/pith-number/D3N3L53BHRWQ32BYPIUW4QMQCW/events.json","paper":"https://pith.science/paper/D3N3L53B"},"agent_actions":{"view_html":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW","download_json":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW.json","view_paper":"https://pith.science/paper/D3N3L53B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1310.7780&json=true","fetch_graph":"https://pith.science/api/pith-number/D3N3L53BHRWQ32BYPIUW4QMQCW/graph.json","fetch_events":"https://pith.science/api/pith-number/D3N3L53BHRWQ32BYPIUW4QMQCW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW/action/storage_attestation","attest_author":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW/action/author_attestation","sign_citation":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW/action/citation_signature","submit_replication":"https://pith.science/pith/D3N3L53BHRWQ32BYPIUW4QMQCW/action/replication_record"}},"created_at":"2026-05-18T02:52:55.939496+00:00","updated_at":"2026-05-18T02:52:55.939496+00:00"}