{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:JHLH2ODYF6UYBMTJH45A6QDPRT","short_pith_number":"pith:JHLH2ODY","canonical_record":{"source":{"id":"1212.4777","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-12-19T18:14:51Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"acd4993b59f200e326b5a34a9c10c9773cdf8868e7c5bd79927ba165b9f5933e","abstract_canon_sha256":"f2a1a1ae11836dd111fcc29d45bb2e24ed590bf42c7f08f8d51d0a93db0ef930"},"schema_version":"1.0"},"canonical_sha256":"49d67d38782fa980b2693f3a0f406f8ce707194345f10af51c27da38405deac0","source":{"kind":"arxiv","id":"1212.4777","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.4777","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"arxiv_version","alias_value":"1212.4777v1","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.4777","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"pith_short_12","alias_value":"JHLH2ODYF6UY","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"JHLH2ODYF6UYBMTJ","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"JHLH2ODY","created_at":"2026-05-18T12:27:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:JHLH2ODYF6UYBMTJH45A6QDPRT","target":"record","payload":{"canonical_record":{"source":{"id":"1212.4777","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-12-19T18:14:51Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"acd4993b59f200e326b5a34a9c10c9773cdf8868e7c5bd79927ba165b9f5933e","abstract_canon_sha256":"f2a1a1ae11836dd111fcc29d45bb2e24ed590bf42c7f08f8d51d0a93db0ef930"},"schema_version":"1.0"},"canonical_sha256":"49d67d38782fa980b2693f3a0f406f8ce707194345f10af51c27da38405deac0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:38:05.395873Z","signature_b64":"LqL9IEnIPBEBgh5DFV+jDRCUaTL43jT7IOSzQ9Blgr2tQOWfFZNFzl1smiXwVdZuqn/+OG+5oH9vg/hOLgh3BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49d67d38782fa980b2693f3a0f406f8ce707194345f10af51c27da38405deac0","last_reissued_at":"2026-05-18T03:38:05.395197Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:38:05.395197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1212.4777","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:38:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8+AvSKYgh2bds15nD9e3mRkdC0XkEbXnFc3h60zlovn6oy46CGvyrmLKo1xU2O7CKYQi96GulsXCyQGr4As4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T09:31:05.083973Z"},"content_sha256":"4024fe3cf3d52e5faf5abba6f396d61537a8301b97fc6835fbc59d27aa71f7c9","schema_version":"1.0","event_id":"sha256:4024fe3cf3d52e5faf5abba6f396d61537a8301b97fc6835fbc59d27aa71f7c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:JHLH2ODYF6UYBMTJH45A6QDPRT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Practical Algorithm for Topic Modeling with Provable Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ankur Moitra, David Mimno, David Sontag, Michael Zhu, Rong Ge, Sanjeev Arora, Yichen Wu, Yoni Halpern","submitted_at":"2012-12-19T18:14:51Z","abstract_excerpt":"Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.4777","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:38:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zirmF+dK/6N8rFMCDyy4OhHADO/d0zDIwHk5jWuV8btdodQedMFbEPGTWQB6FTvW1arEmXbJXuALDG7dG4SaCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T09:31:05.084530Z"},"content_sha256":"0720f6f11959239bd9bedd3c6c6816c4cc26b0552220d0a7d95aed983d7d5d73","schema_version":"1.0","event_id":"sha256:0720f6f11959239bd9bedd3c6c6816c4cc26b0552220d0a7d95aed983d7d5d73"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/bundle.json","state_url":"https://pith.science/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T09:31:05Z","links":{"resolver":"https://pith.science/pith/JHLH2ODYF6UYBMTJH45A6QDPRT","bundle":"https://pith.science/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/bundle.json","state":"https://pith.science/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JHLH2ODYF6UYBMTJH45A6QDPRT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:JHLH2ODYF6UYBMTJH45A6QDPRT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f2a1a1ae11836dd111fcc29d45bb2e24ed590bf42c7f08f8d51d0a93db0ef930","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-12-19T18:14:51Z","title_canon_sha256":"acd4993b59f200e326b5a34a9c10c9773cdf8868e7c5bd79927ba165b9f5933e"},"schema_version":"1.0","source":{"id":"1212.4777","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.4777","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"arxiv_version","alias_value":"1212.4777v1","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.4777","created_at":"2026-05-18T03:38:05Z"},{"alias_kind":"pith_short_12","alias_value":"JHLH2ODYF6UY","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"JHLH2ODYF6UYBMTJ","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"JHLH2ODY","created_at":"2026-05-18T12:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:0720f6f11959239bd9bedd3c6c6816c4cc26b0552220d0a7d95aed983d7d5d73","target":"graph","created_at":"2026-05-18T03:38:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical.","authors_text":"Ankur Moitra, David Mimno, David Sontag, Michael Zhu, Rong Ge, Sanjeev Arora, Yichen Wu, Yoni Halpern","cross_cats":["cs.DS","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-12-19T18:14:51Z","title":"A Practical Algorithm for Topic Modeling with Provable Guarantees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.4777","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4024fe3cf3d52e5faf5abba6f396d61537a8301b97fc6835fbc59d27aa71f7c9","target":"record","created_at":"2026-05-18T03:38:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f2a1a1ae11836dd111fcc29d45bb2e24ed590bf42c7f08f8d51d0a93db0ef930","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-12-19T18:14:51Z","title_canon_sha256":"acd4993b59f200e326b5a34a9c10c9773cdf8868e7c5bd79927ba165b9f5933e"},"schema_version":"1.0","source":{"id":"1212.4777","kind":"arxiv","version":1}},"canonical_sha256":"49d67d38782fa980b2693f3a0f406f8ce707194345f10af51c27da38405deac0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49d67d38782fa980b2693f3a0f406f8ce707194345f10af51c27da38405deac0","first_computed_at":"2026-05-18T03:38:05.395197Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:38:05.395197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LqL9IEnIPBEBgh5DFV+jDRCUaTL43jT7IOSzQ9Blgr2tQOWfFZNFzl1smiXwVdZuqn/+OG+5oH9vg/hOLgh3BA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:38:05.395873Z","signed_message":"canonical_sha256_bytes"},"source_id":"1212.4777","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4024fe3cf3d52e5faf5abba6f396d61537a8301b97fc6835fbc59d27aa71f7c9","sha256:0720f6f11959239bd9bedd3c6c6816c4cc26b0552220d0a7d95aed983d7d5d73"],"state_sha256":"fe1eea1128c6e37bb550bff8dd998567427c2bd5383fb18d7b7d2294001d756d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"usFSvXkNTh4fp3Zdy/QgEFVG9Y1c1+mQlu2iD0JrAf0gK40w5sQTjqrOYwRirHhFpon65re5RgIOVfDyMfzFDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T09:31:05.087341Z","bundle_sha256":"d20bac4dacec8520b45c6de16f459eb4f65a5eee2d418a1e2a33263d77128e94"}}