{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CRS4IJXDDYT6M5V36BNCFFJQ4T","short_pith_number":"pith:CRS4IJXD","canonical_record":{"source":{"id":"1705.06884","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T08:05:10Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"aa00dda38528ed71469139fb1611dfaf1c9c56d431ee3778c38aa9d21d756a96","abstract_canon_sha256":"784e77c9c6c4fd7f9294d555efbc15cfca5366184ddb90fe81fb0c584f67fcb5"},"schema_version":"1.0"},"canonical_sha256":"1465c426e31e27e676bbf05a229530e4e7f9ec5a71a9e737d216fd9dfb241e0c","source":{"kind":"arxiv","id":"1705.06884","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06884","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06884v2","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06884","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"CRS4IJXDDYT6","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CRS4IJXDDYT6M5V3","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CRS4IJXD","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CRS4IJXDDYT6M5V36BNCFFJQ4T","target":"record","payload":{"canonical_record":{"source":{"id":"1705.06884","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T08:05:10Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"aa00dda38528ed71469139fb1611dfaf1c9c56d431ee3778c38aa9d21d756a96","abstract_canon_sha256":"784e77c9c6c4fd7f9294d555efbc15cfca5366184ddb90fe81fb0c584f67fcb5"},"schema_version":"1.0"},"canonical_sha256":"1465c426e31e27e676bbf05a229530e4e7f9ec5a71a9e737d216fd9dfb241e0c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:06.517229Z","signature_b64":"fPBy2LJT0EDKvV6+3Xive+PqwJ+45q28nfFaybJTz+n70mi5FENl/fNYMT1x7Bog6+E+3E1KQjRU8o1/IbVCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1465c426e31e27e676bbf05a229530e4e7f9ec5a71a9e737d216fd9dfb241e0c","last_reissued_at":"2026-05-18T00:44:06.516511Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:06.516511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.06884","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yqj1yms2/Vr/VWJbhWPek7DC3TDzDu/1KFhcTvIhNbqsBj3/EyoQYNA+VmqyMRxi84kdddiSOPJU4fQrGxexCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T19:33:55.794198Z"},"content_sha256":"6216d0cb61fb69a4f48e2b1cd59ccd7f18b5e81f247fe57848c5d0a41f994804","schema_version":"1.0","event_id":"sha256:6216d0cb61fb69a4f48e2b1cd59ccd7f18b5e81f247fe57848c5d0a41f994804"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CRS4IJXDDYT6M5V36BNCFFJQ4T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Unified Framework for Stochastic Matrix Factorization via Variance Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Jiashi Feng, Renbo Zhao, William B. Haskell","submitted_at":"2017-05-19T08:05:10Z","abstract_excerpt":"We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several well-known SMF formulations in the literature. We perform a non-asymptotic convergence analysis of our framework and derive computational and sample complexities for our algorithm to converge to an $\\epsilon$-stationary point in expectation. In addition, extensive experiments for a wide class of SMF formulations demonstrate that our framework consistently yields faster convergence and a more accurate output dictionary "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06884","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:44:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rLhlOjFaKrj6QeErw8KpCf5Pb6xuH9YGzDYeH2oFpz//QwnGH8z53H8o2A2I256q8pUN4sexl5TbvK/SFO0SDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T19:33:55.794870Z"},"content_sha256":"7908cf10c303af89a6d2076c6ce9d773ead585d78b09bf05c0bb3679435e4b03","schema_version":"1.0","event_id":"sha256:7908cf10c303af89a6d2076c6ce9d773ead585d78b09bf05c0bb3679435e4b03"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/bundle.json","state_url":"https://pith.science/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/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":"2026-05-29T19:33:55Z","links":{"resolver":"https://pith.science/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T","bundle":"https://pith.science/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/bundle.json","state":"https://pith.science/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CRS4IJXDDYT6M5V36BNCFFJQ4T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CRS4IJXDDYT6M5V36BNCFFJQ4T","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":"784e77c9c6c4fd7f9294d555efbc15cfca5366184ddb90fe81fb0c584f67fcb5","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T08:05:10Z","title_canon_sha256":"aa00dda38528ed71469139fb1611dfaf1c9c56d431ee3778c38aa9d21d756a96"},"schema_version":"1.0","source":{"id":"1705.06884","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.06884","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"arxiv_version","alias_value":"1705.06884v2","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.06884","created_at":"2026-05-18T00:44:06Z"},{"alias_kind":"pith_short_12","alias_value":"CRS4IJXDDYT6","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CRS4IJXDDYT6M5V3","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CRS4IJXD","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:7908cf10c303af89a6d2076c6ce9d773ead585d78b09bf05c0bb3679435e4b03","target":"graph","created_at":"2026-05-18T00:44:06Z","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":"We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several well-known SMF formulations in the literature. We perform a non-asymptotic convergence analysis of our framework and derive computational and sample complexities for our algorithm to converge to an $\\epsilon$-stationary point in expectation. In addition, extensive experiments for a wide class of SMF formulations demonstrate that our framework consistently yields faster convergence and a more accurate output dictionary ","authors_text":"Jiashi Feng, Renbo Zhao, William B. Haskell","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T08:05:10Z","title":"A Unified Framework for Stochastic Matrix Factorization via Variance Reduction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.06884","kind":"arxiv","version":2},"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":"sha256:6216d0cb61fb69a4f48e2b1cd59ccd7f18b5e81f247fe57848c5d0a41f994804","target":"record","created_at":"2026-05-18T00:44:06Z","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":"784e77c9c6c4fd7f9294d555efbc15cfca5366184ddb90fe81fb0c584f67fcb5","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T08:05:10Z","title_canon_sha256":"aa00dda38528ed71469139fb1611dfaf1c9c56d431ee3778c38aa9d21d756a96"},"schema_version":"1.0","source":{"id":"1705.06884","kind":"arxiv","version":2}},"canonical_sha256":"1465c426e31e27e676bbf05a229530e4e7f9ec5a71a9e737d216fd9dfb241e0c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1465c426e31e27e676bbf05a229530e4e7f9ec5a71a9e737d216fd9dfb241e0c","first_computed_at":"2026-05-18T00:44:06.516511Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:06.516511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fPBy2LJT0EDKvV6+3Xive+PqwJ+45q28nfFaybJTz+n70mi5FENl/fNYMT1x7Bog6+E+3E1KQjRU8o1/IbVCAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:06.517229Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.06884","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6216d0cb61fb69a4f48e2b1cd59ccd7f18b5e81f247fe57848c5d0a41f994804","sha256:7908cf10c303af89a6d2076c6ce9d773ead585d78b09bf05c0bb3679435e4b03"],"state_sha256":"1e115fe7a02241d3f92a4f1c4e416e1dfdb86b8dc0f8b5f0fa6cdc3e4c25fa20"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iS403jj6b9r+cbX9WS/UgeBgnVOO+d5+6mZkC+ZmJi9qH6ByJagYB9Ph57pfnsOPaxLPrGaVU3ovurT/thE8AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T19:33:55.806641Z","bundle_sha256":"fe6ea809f5c550269ba12df7d228bc004264028398a114f0f304d2d80154b1d6"}}