{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NHE2UFET5NIOSM5A3RAOAE22ZO","short_pith_number":"pith:NHE2UFET","canonical_record":{"source":{"id":"1901.08689","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T23:37:52Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"09f9dd8aea349622783a0301ead61cf081c0eaa69738b76b2f352e458f95edf2","abstract_canon_sha256":"41950c92f2104dec8cc5155fecb14c65594437cb6f4f1bb2f380304f630e15ae"},"schema_version":"1.0"},"canonical_sha256":"69c9aa1493eb50e933a0dc40e0135acb882c35d826c3a7126ea876eef83252b4","source":{"kind":"arxiv","id":"1901.08689","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08689","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08689v2","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08689","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"pith_short_12","alias_value":"NHE2UFET5NIO","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NHE2UFET5NIOSM5A","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NHE2UFET","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NHE2UFET5NIOSM5A3RAOAE22ZO","target":"record","payload":{"canonical_record":{"source":{"id":"1901.08689","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T23:37:52Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"09f9dd8aea349622783a0301ead61cf081c0eaa69738b76b2f352e458f95edf2","abstract_canon_sha256":"41950c92f2104dec8cc5155fecb14c65594437cb6f4f1bb2f380304f630e15ae"},"schema_version":"1.0"},"canonical_sha256":"69c9aa1493eb50e933a0dc40e0135acb882c35d826c3a7126ea876eef83252b4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:07.208081Z","signature_b64":"rNPCMrRrXdsWJc8CBFrjHsiNOcoZPNFvyRti9oGjNCb+KtHpNe6G9mfH2OS1AUmcSM90H72Vrixq2a4I84haDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69c9aa1493eb50e933a0dc40e0135acb882c35d826c3a7126ea876eef83252b4","last_reissued_at":"2026-05-17T23:44:07.207600Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:07.207600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.08689","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":"2026-05-17T23:44:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FmerIlv7jN4cXAstwa35Ixol9Wnc7uQOFV4klSqUQ3rAXMznJs9FRdXwxbMKQrt3g1AphN9o8DuABlEi+QJxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:16:23.926012Z"},"content_sha256":"041fcacb2ac37365886f4943836735e91544381adc7a585280934b241cc5c444","schema_version":"1.0","event_id":"sha256:041fcacb2ac37365886f4943836735e91544381adc7a585280934b241cc5c444"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NHE2UFET5NIOSM5A3RAOAE22ZO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Dmitry Kovalev, Peter Richtarik, Samuel Horvath","submitted_at":"2019-01-24T23:37:52Z","abstract_excerpt":"The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structural element in both of these methods is the inclusion of an outer loop at the beginning of which a full pass over the training data is made in order to compute the exact gradient, which is then used to construct a variance-reduced estimator "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08689","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":"2026-05-17T23:44:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"myXNxtwGjzAP988Q+7OKHYuAAhIgCuOttbVL7tBTZvDdvRg1Nkjx7OJqj8VEDXb8pPUS5VGeiCEoej5D3usTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T16:16:23.926672Z"},"content_sha256":"18bf6c352c656085e057ac5d4d2ce8340c95d688473a1dd9c28d7f9e4cebd32a","schema_version":"1.0","event_id":"sha256:18bf6c352c656085e057ac5d4d2ce8340c95d688473a1dd9c28d7f9e4cebd32a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/bundle.json","state_url":"https://pith.science/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/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":"2026-05-29T16:16:23Z","links":{"resolver":"https://pith.science/pith/NHE2UFET5NIOSM5A3RAOAE22ZO","bundle":"https://pith.science/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/bundle.json","state":"https://pith.science/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NHE2UFET5NIOSM5A3RAOAE22ZO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NHE2UFET5NIOSM5A3RAOAE22ZO","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":"41950c92f2104dec8cc5155fecb14c65594437cb6f4f1bb2f380304f630e15ae","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T23:37:52Z","title_canon_sha256":"09f9dd8aea349622783a0301ead61cf081c0eaa69738b76b2f352e458f95edf2"},"schema_version":"1.0","source":{"id":"1901.08689","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.08689","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"arxiv_version","alias_value":"1901.08689v2","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08689","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"pith_short_12","alias_value":"NHE2UFET5NIO","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NHE2UFET5NIOSM5A","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NHE2UFET","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:18bf6c352c656085e057ac5d4d2ce8340c95d688473a1dd9c28d7f9e4cebd32a","target":"graph","created_at":"2026-05-17T23:44:07Z","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":"The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few years due to their superior theoretical properties and empirical behaviour on training supervised machine learning models via the empirical risk minimization paradigm. A key structural element in both of these methods is the inclusion of an outer loop at the beginning of which a full pass over the training data is made in order to compute the exact gradient, which is then used to construct a variance-reduced estimator ","authors_text":"Dmitry Kovalev, Peter Richtarik, Samuel Horvath","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T23:37:52Z","title":"Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08689","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":"sha256:041fcacb2ac37365886f4943836735e91544381adc7a585280934b241cc5c444","target":"record","created_at":"2026-05-17T23:44:07Z","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":"41950c92f2104dec8cc5155fecb14c65594437cb6f4f1bb2f380304f630e15ae","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T23:37:52Z","title_canon_sha256":"09f9dd8aea349622783a0301ead61cf081c0eaa69738b76b2f352e458f95edf2"},"schema_version":"1.0","source":{"id":"1901.08689","kind":"arxiv","version":2}},"canonical_sha256":"69c9aa1493eb50e933a0dc40e0135acb882c35d826c3a7126ea876eef83252b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"69c9aa1493eb50e933a0dc40e0135acb882c35d826c3a7126ea876eef83252b4","first_computed_at":"2026-05-17T23:44:07.207600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:07.207600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rNPCMrRrXdsWJc8CBFrjHsiNOcoZPNFvyRti9oGjNCb+KtHpNe6G9mfH2OS1AUmcSM90H72Vrixq2a4I84haDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:07.208081Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.08689","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:041fcacb2ac37365886f4943836735e91544381adc7a585280934b241cc5c444","sha256:18bf6c352c656085e057ac5d4d2ce8340c95d688473a1dd9c28d7f9e4cebd32a"],"state_sha256":"2a64ed70f50e65dd502c9e933a06abe1d02fca41b21e56cf11aa75e05ad5da26"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OwaRF+UilZJ5Pwzl9mVbqpLjFVTS1iuoZbjIjChOpNjaCUYpzhOjD70xpm7GZZUWMjiAYwlo3d5//EJshn9FBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T16:16:23.930245Z","bundle_sha256":"4a6d2f97f1a98a4c36ec3082019d8eabc7da4246bef39d1539c54776875e244c"}}