{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:GQFWZCCBWPCDDVUPX54WCYVMWP","short_pith_number":"pith:GQFWZCCB","canonical_record":{"source":{"id":"1403.0603","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-03-03T21:32:53Z","cross_cats_sorted":["cs.DC","cs.SY","math.IT","math.OC"],"title_canon_sha256":"487923b6c6a9d314d48fcc6b3879d87a757b6ae148f7704f2566cb5b5d8cd1ae","abstract_canon_sha256":"6480d8e7ac1fe7579d80b87fe4c0b46d297025e284599161300d02647ec36088"},"schema_version":"1.0"},"canonical_sha256":"340b6c8841b3c431d68fbf796162acb3ef8665a82491fb27df31880484d8eb26","source":{"kind":"arxiv","id":"1403.0603","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.0603","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"arxiv_version","alias_value":"1403.0603v2","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.0603","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"pith_short_12","alias_value":"GQFWZCCBWPCD","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"GQFWZCCBWPCDDVUP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"GQFWZCCB","created_at":"2026-05-18T12:28:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:GQFWZCCBWPCDDVUPX54WCYVMWP","target":"record","payload":{"canonical_record":{"source":{"id":"1403.0603","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-03-03T21:32:53Z","cross_cats_sorted":["cs.DC","cs.SY","math.IT","math.OC"],"title_canon_sha256":"487923b6c6a9d314d48fcc6b3879d87a757b6ae148f7704f2566cb5b5d8cd1ae","abstract_canon_sha256":"6480d8e7ac1fe7579d80b87fe4c0b46d297025e284599161300d02647ec36088"},"schema_version":"1.0"},"canonical_sha256":"340b6c8841b3c431d68fbf796162acb3ef8665a82491fb27df31880484d8eb26","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:57:06.097481Z","signature_b64":"kJt1+SLoa12YwdMxnkxD0AVDbyMDM7ySL5P5IjC9R+QJY83jbGqRBNPHa97c6W/TUUqv+LzBGfAg3u4h+51TCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"340b6c8841b3c431d68fbf796162acb3ef8665a82491fb27df31880484d8eb26","last_reissued_at":"2026-05-18T02:57:06.096799Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:57:06.096799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1403.0603","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":"2026-05-18T02:57:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nk3dBY78RZl21KuPfmFeSwlngVw2HpPyeToBo5QoCY1gu9daj2qT4EY5znm6VBYJrwx8PgxN1/N0++m9aly+DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:18:03.663883Z"},"content_sha256":"034bc7326283f79f587e51ffa16643ad0306a3406f8546795950981915aefe8f","schema_version":"1.0","event_id":"sha256:034bc7326283f79f587e51ffa16643ad0306a3406f8546795950981915aefe8f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:GQFWZCCBWPCDDVUPX54WCYVMWP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Distributed Online Prediction and Stochastic Optimization with Approximate Distributed Averaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.SY","math.IT","math.OC"],"primary_cat":"cs.IT","authors_text":"Konstantinos I. Tsianos, Michael G. Rabbat","submitted_at":"2014-03-03T21:32:53Z","abstract_excerpt":"We study distributed methods for online prediction and stochastic optimization. Our approach is iterative: in each round nodes first perform local computations and then communicate in order to aggregate information and synchronize their decision variables. Synchronization is accomplished through the use of a distributed averaging protocol. When an exact distributed averaging protocol is used, it is known that the optimal regret bound of $\\mathcal{O}(\\sqrt{m})$ can be achieved using the distributed mini-batch algorithm of Dekel et al. (2012), where $m$ is the total number of samples processed a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.0603","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":"2026-05-18T02:57:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eeHc5w1YtBZIDqEpDqmYmE950tUkvsu+ufDmuE6Kr3D0kBzsDICiPmffIeGbLnS2blB+54dkGjk85YfcFCpjDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T01:18:03.664571Z"},"content_sha256":"e04bff6893fd9d1f28eb4519cb640fd9ed59d367fdc1583a654072798b4b7406","schema_version":"1.0","event_id":"sha256:e04bff6893fd9d1f28eb4519cb640fd9ed59d367fdc1583a654072798b4b7406"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/bundle.json","state_url":"https://pith.science/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/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":"2026-05-31T01:18:03Z","links":{"resolver":"https://pith.science/pith/GQFWZCCBWPCDDVUPX54WCYVMWP","bundle":"https://pith.science/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/bundle.json","state":"https://pith.science/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GQFWZCCBWPCDDVUPX54WCYVMWP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:GQFWZCCBWPCDDVUPX54WCYVMWP","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":"6480d8e7ac1fe7579d80b87fe4c0b46d297025e284599161300d02647ec36088","cross_cats_sorted":["cs.DC","cs.SY","math.IT","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-03-03T21:32:53Z","title_canon_sha256":"487923b6c6a9d314d48fcc6b3879d87a757b6ae148f7704f2566cb5b5d8cd1ae"},"schema_version":"1.0","source":{"id":"1403.0603","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.0603","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"arxiv_version","alias_value":"1403.0603v2","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.0603","created_at":"2026-05-18T02:57:06Z"},{"alias_kind":"pith_short_12","alias_value":"GQFWZCCBWPCD","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"GQFWZCCBWPCDDVUP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"GQFWZCCB","created_at":"2026-05-18T12:28:30Z"}],"graph_snapshots":[{"event_id":"sha256:e04bff6893fd9d1f28eb4519cb640fd9ed59d367fdc1583a654072798b4b7406","target":"graph","created_at":"2026-05-18T02:57:06Z","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 study distributed methods for online prediction and stochastic optimization. Our approach is iterative: in each round nodes first perform local computations and then communicate in order to aggregate information and synchronize their decision variables. Synchronization is accomplished through the use of a distributed averaging protocol. When an exact distributed averaging protocol is used, it is known that the optimal regret bound of $\\mathcal{O}(\\sqrt{m})$ can be achieved using the distributed mini-batch algorithm of Dekel et al. (2012), where $m$ is the total number of samples processed a","authors_text":"Konstantinos I. Tsianos, Michael G. Rabbat","cross_cats":["cs.DC","cs.SY","math.IT","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-03-03T21:32:53Z","title":"Efficient Distributed Online Prediction and Stochastic Optimization with Approximate Distributed Averaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.0603","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":"sha256:034bc7326283f79f587e51ffa16643ad0306a3406f8546795950981915aefe8f","target":"record","created_at":"2026-05-18T02:57:06Z","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":"6480d8e7ac1fe7579d80b87fe4c0b46d297025e284599161300d02647ec36088","cross_cats_sorted":["cs.DC","cs.SY","math.IT","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2014-03-03T21:32:53Z","title_canon_sha256":"487923b6c6a9d314d48fcc6b3879d87a757b6ae148f7704f2566cb5b5d8cd1ae"},"schema_version":"1.0","source":{"id":"1403.0603","kind":"arxiv","version":2}},"canonical_sha256":"340b6c8841b3c431d68fbf796162acb3ef8665a82491fb27df31880484d8eb26","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"340b6c8841b3c431d68fbf796162acb3ef8665a82491fb27df31880484d8eb26","first_computed_at":"2026-05-18T02:57:06.096799Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:57:06.096799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kJt1+SLoa12YwdMxnkxD0AVDbyMDM7ySL5P5IjC9R+QJY83jbGqRBNPHa97c6W/TUUqv+LzBGfAg3u4h+51TCA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:57:06.097481Z","signed_message":"canonical_sha256_bytes"},"source_id":"1403.0603","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:034bc7326283f79f587e51ffa16643ad0306a3406f8546795950981915aefe8f","sha256:e04bff6893fd9d1f28eb4519cb640fd9ed59d367fdc1583a654072798b4b7406"],"state_sha256":"af1a0feaffd4542cd3ad6117afd67bb014ff4c3cb8938c8587dad763c4d2c69d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vvHe8FI9KzgKLp30AjAOnhaWYU8tq0n+oBK9E3uGX9wCF9DFdmlNRatEY74a7T3LM/3LQ1Z58kB4f7KErgDYBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T01:18:03.668646Z","bundle_sha256":"e4b9ba4d5835442d98c1890c0ecbce47b42a49245549099b2d7272c30bc02ed9"}}