{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VNGNKOWDV32J2F3VXBG3T4ZLJP","short_pith_number":"pith:VNGNKOWD","canonical_record":{"source":{"id":"1810.03105","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-07T08:43:05Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"bbd8f86051d463bd3e0b2b2c727892aaf66d912fe45d8257110151d945bba081","abstract_canon_sha256":"62459e7147455c06d3a886ab69e21a0e38224390fe92e90d4e0ad1a2aaeee25b"},"schema_version":"1.0"},"canonical_sha256":"ab4cd53ac3aef49d1775b84db9f32b4bf027348be822a3034b13bb0a2a33bf77","source":{"kind":"arxiv","id":"1810.03105","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.03105","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"arxiv_version","alias_value":"1810.03105v2","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03105","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"pith_short_12","alias_value":"VNGNKOWDV32J","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VNGNKOWDV32J2F3V","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VNGNKOWD","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VNGNKOWDV32J2F3VXBG3T4ZLJP","target":"record","payload":{"canonical_record":{"source":{"id":"1810.03105","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-07T08:43:05Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"bbd8f86051d463bd3e0b2b2c727892aaf66d912fe45d8257110151d945bba081","abstract_canon_sha256":"62459e7147455c06d3a886ab69e21a0e38224390fe92e90d4e0ad1a2aaeee25b"},"schema_version":"1.0"},"canonical_sha256":"ab4cd53ac3aef49d1775b84db9f32b4bf027348be822a3034b13bb0a2a33bf77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:29.561684Z","signature_b64":"1wUcZJGUn7nowBANT26qaf2iwlJYQUxmoAqhNNqJTjFopLgly+D0ywz5FCyu1Ws/QDeDXmxQDxW13QXgDrlSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab4cd53ac3aef49d1775b84db9f32b4bf027348be822a3034b13bb0a2a33bf77","last_reissued_at":"2026-05-18T00:00:29.561191Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:29.561191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.03105","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":"2026-05-18T00:00:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v+aD/hdTAGy6RLPIWnzuV8PaBavZWpDdS6U+xUoTv79PLWgJRc1aXk9Ik7RgNFankHbIRGGCoDXFl4MhjUNGDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:59:41.398743Z"},"content_sha256":"3b9a79c1c5805709015555c6141288af423b64751e4de69ceb69485dc3830703","schema_version":"1.0","event_id":"sha256:3b9a79c1c5805709015555c6141288af423b64751e4de69ceb69485dc3830703"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VNGNKOWDV32J2F3VXBG3T4ZLJP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ASVRG: Accelerated Proximal SVRG","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fanhua Shang, James Cheng, Kaiwen Zhou, Licheng Jiao, Yan Ren, Yufei Jin","submitted_at":"2018-10-07T08:43:05Z","abstract_excerpt":"This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick. Unlike most existing accelerated stochastic variance reduction methods such as Katyusha, ASVRG has only one additional variable and one momentum parameter. Thus, ASVRG is much simpler than those methods, and has much lower per-iteration complexity. We prove that ASVRG achieves the best known oracle complexities for both strongly convex and non-strongly convex objectives. In addition, we extend ASVRG to mini-batch and non-smooth "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03105","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":"2026-05-18T00:00:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WrqpEHtYmAk44NIP/HQ8krPdkCsXv8ViX2xQt7zre0nWTcV1qCBJ84vKleTm+07yuNSbAC6h9yGJwzH1qqUfCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T12:59:41.399372Z"},"content_sha256":"8719c08225d231e7a9c04c9f71f92c74e7b66b4b561348cc2f4a332666e15e28","schema_version":"1.0","event_id":"sha256:8719c08225d231e7a9c04c9f71f92c74e7b66b4b561348cc2f4a332666e15e28"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/bundle.json","state_url":"https://pith.science/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/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":"2026-06-08T12:59:41Z","links":{"resolver":"https://pith.science/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP","bundle":"https://pith.science/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/bundle.json","state":"https://pith.science/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VNGNKOWDV32J2F3VXBG3T4ZLJP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VNGNKOWDV32J2F3VXBG3T4ZLJP","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":"62459e7147455c06d3a886ab69e21a0e38224390fe92e90d4e0ad1a2aaeee25b","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-07T08:43:05Z","title_canon_sha256":"bbd8f86051d463bd3e0b2b2c727892aaf66d912fe45d8257110151d945bba081"},"schema_version":"1.0","source":{"id":"1810.03105","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.03105","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"arxiv_version","alias_value":"1810.03105v2","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03105","created_at":"2026-05-18T00:00:29Z"},{"alias_kind":"pith_short_12","alias_value":"VNGNKOWDV32J","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VNGNKOWDV32J2F3V","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VNGNKOWD","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:8719c08225d231e7a9c04c9f71f92c74e7b66b4b561348cc2f4a332666e15e28","target":"graph","created_at":"2026-05-18T00:00:29Z","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":"This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick. Unlike most existing accelerated stochastic variance reduction methods such as Katyusha, ASVRG has only one additional variable and one momentum parameter. Thus, ASVRG is much simpler than those methods, and has much lower per-iteration complexity. We prove that ASVRG achieves the best known oracle complexities for both strongly convex and non-strongly convex objectives. In addition, we extend ASVRG to mini-batch and non-smooth ","authors_text":"Fanhua Shang, James Cheng, Kaiwen Zhou, Licheng Jiao, Yan Ren, Yufei Jin","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-07T08:43:05Z","title":"ASVRG: Accelerated Proximal SVRG"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03105","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":"sha256:3b9a79c1c5805709015555c6141288af423b64751e4de69ceb69485dc3830703","target":"record","created_at":"2026-05-18T00:00:29Z","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":"62459e7147455c06d3a886ab69e21a0e38224390fe92e90d4e0ad1a2aaeee25b","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-07T08:43:05Z","title_canon_sha256":"bbd8f86051d463bd3e0b2b2c727892aaf66d912fe45d8257110151d945bba081"},"schema_version":"1.0","source":{"id":"1810.03105","kind":"arxiv","version":2}},"canonical_sha256":"ab4cd53ac3aef49d1775b84db9f32b4bf027348be822a3034b13bb0a2a33bf77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab4cd53ac3aef49d1775b84db9f32b4bf027348be822a3034b13bb0a2a33bf77","first_computed_at":"2026-05-18T00:00:29.561191Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:29.561191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1wUcZJGUn7nowBANT26qaf2iwlJYQUxmoAqhNNqJTjFopLgly+D0ywz5FCyu1Ws/QDeDXmxQDxW13QXgDrlSCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:29.561684Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.03105","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b9a79c1c5805709015555c6141288af423b64751e4de69ceb69485dc3830703","sha256:8719c08225d231e7a9c04c9f71f92c74e7b66b4b561348cc2f4a332666e15e28"],"state_sha256":"8f71106bd65151faab5fbbcada9d9c492c5db4f9ac948cf6ad277c44752e85fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sQhm8JK+2frr22v86t4blRxaQz60DKqP9tPAVFTPfew1ISHmqSp7cl7igkh2llXSiqBQ27NJ6+V7U+FtGhpfBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T12:59:41.402455Z","bundle_sha256":"cbc8ccfee3a409f799d1f785112489376756fe1593f9ea0f3be7d889dd54e4d9"}}