{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IEOZ6JEAWX7I2PFRC32MNU62MY","short_pith_number":"pith:IEOZ6JEA","canonical_record":{"source":{"id":"1805.10378","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T22:02:30Z","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"title_canon_sha256":"70aee4cffb3c1c35dfc34f06cc95ee84f3eb4f10d6ae0b3a6ac6f78541a1a9a0","abstract_canon_sha256":"b4ec07f2d8055abf7b3b7c233296ff0e1d4bd8a839796be6d49f7fa181fe030b"},"schema_version":"1.0"},"canonical_sha256":"411d9f2480b5fe8d3cb116f4c6d3da66131ae9523da39ed8565c832853475e6a","source":{"kind":"arxiv","id":"1805.10378","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10378","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10378v1","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10378","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"pith_short_12","alias_value":"IEOZ6JEAWX7I","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"IEOZ6JEAWX7I2PFR","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"IEOZ6JEA","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IEOZ6JEAWX7I2PFRC32MNU62MY","target":"record","payload":{"canonical_record":{"source":{"id":"1805.10378","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T22:02:30Z","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"title_canon_sha256":"70aee4cffb3c1c35dfc34f06cc95ee84f3eb4f10d6ae0b3a6ac6f78541a1a9a0","abstract_canon_sha256":"b4ec07f2d8055abf7b3b7c233296ff0e1d4bd8a839796be6d49f7fa181fe030b"},"schema_version":"1.0"},"canonical_sha256":"411d9f2480b5fe8d3cb116f4c6d3da66131ae9523da39ed8565c832853475e6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:52.729126Z","signature_b64":"8NEUNsbF+m50qtDRlFRkvPjzAknQvLLfU+0Y6qJAol9MTeM5AZBnkMXK0HMzNPds5hDMKM0MEqm+8ijBQYV7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"411d9f2480b5fe8d3cb116f4c6d3da66131ae9523da39ed8565c832853475e6a","last_reissued_at":"2026-05-18T00:14:52.728438Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:52.728438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.10378","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":"2026-05-18T00:14:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l/UUtA8DRUTc3hDfUT7Ikf2Kfhmur50Sv3e2zVRZAHc7KbvDIlgEHCQBHY3PLnxFTOVMnP/YBO5l41l3xM7CBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T11:45:50.289004Z"},"content_sha256":"33da6f18784b25a60820e2a918cf011e72ae776ad38ef70b809046b7e26930c1","schema_version":"1.0","event_id":"sha256:33da6f18784b25a60820e2a918cf011e72ae776ad38ef70b809046b7e26930c1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IEOZ6JEAWX7I2PFRC32MNU62MY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gradient Coding via the Stochastic Block Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"primary_cat":"stat.ML","authors_text":"Dimitris Papailiopoulos, Zachary Charles","submitted_at":"2018-05-25T22:02:30Z","abstract_excerpt":"Gradient descent and its many variants, including mini-batch stochastic gradient descent, form the algorithmic foundation of modern large-scale machine learning. Due to the size and scale of modern data, gradient computations are often distributed across multiple compute nodes. Unfortunately, such distributed implementations can face significant delays caused by straggler nodes, i.e., nodes that are much slower than average. Gradient coding is a new technique for mitigating the effect of stragglers via algorithmic redundancy. While effective, previously proposed gradient codes can be computati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10378","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":"2026-05-18T00:14:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZWSpv0n6hT2fqdKLaPGIU58Laj9ppZ/Gd+n1J+btxKZHgofY0QUdYaqI4NCcSyqVkkiA9HXn9hxdLi1q/FPUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T11:45:50.289371Z"},"content_sha256":"3a69426b339cd10595c4c3c080bc6f0d9c2ba0526c24f4eb632c97e6012d3593","schema_version":"1.0","event_id":"sha256:3a69426b339cd10595c4c3c080bc6f0d9c2ba0526c24f4eb632c97e6012d3593"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/bundle.json","state_url":"https://pith.science/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/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":"2026-05-28T11:45:50Z","links":{"resolver":"https://pith.science/pith/IEOZ6JEAWX7I2PFRC32MNU62MY","bundle":"https://pith.science/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/bundle.json","state":"https://pith.science/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IEOZ6JEAWX7I2PFRC32MNU62MY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IEOZ6JEAWX7I2PFRC32MNU62MY","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":"b4ec07f2d8055abf7b3b7c233296ff0e1d4bd8a839796be6d49f7fa181fe030b","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T22:02:30Z","title_canon_sha256":"70aee4cffb3c1c35dfc34f06cc95ee84f3eb4f10d6ae0b3a6ac6f78541a1a9a0"},"schema_version":"1.0","source":{"id":"1805.10378","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10378","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10378v1","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10378","created_at":"2026-05-18T00:14:52Z"},{"alias_kind":"pith_short_12","alias_value":"IEOZ6JEAWX7I","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"IEOZ6JEAWX7I2PFR","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"IEOZ6JEA","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:3a69426b339cd10595c4c3c080bc6f0d9c2ba0526c24f4eb632c97e6012d3593","target":"graph","created_at":"2026-05-18T00:14:52Z","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":"Gradient descent and its many variants, including mini-batch stochastic gradient descent, form the algorithmic foundation of modern large-scale machine learning. Due to the size and scale of modern data, gradient computations are often distributed across multiple compute nodes. Unfortunately, such distributed implementations can face significant delays caused by straggler nodes, i.e., nodes that are much slower than average. Gradient coding is a new technique for mitigating the effect of stragglers via algorithmic redundancy. While effective, previously proposed gradient codes can be computati","authors_text":"Dimitris Papailiopoulos, Zachary Charles","cross_cats":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T22:02:30Z","title":"Gradient Coding via the Stochastic Block Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10378","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":"sha256:33da6f18784b25a60820e2a918cf011e72ae776ad38ef70b809046b7e26930c1","target":"record","created_at":"2026-05-18T00:14:52Z","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":"b4ec07f2d8055abf7b3b7c233296ff0e1d4bd8a839796be6d49f7fa181fe030b","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-25T22:02:30Z","title_canon_sha256":"70aee4cffb3c1c35dfc34f06cc95ee84f3eb4f10d6ae0b3a6ac6f78541a1a9a0"},"schema_version":"1.0","source":{"id":"1805.10378","kind":"arxiv","version":1}},"canonical_sha256":"411d9f2480b5fe8d3cb116f4c6d3da66131ae9523da39ed8565c832853475e6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"411d9f2480b5fe8d3cb116f4c6d3da66131ae9523da39ed8565c832853475e6a","first_computed_at":"2026-05-18T00:14:52.728438Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:52.728438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8NEUNsbF+m50qtDRlFRkvPjzAknQvLLfU+0Y6qJAol9MTeM5AZBnkMXK0HMzNPds5hDMKM0MEqm+8ijBQYV7AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:52.729126Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10378","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33da6f18784b25a60820e2a918cf011e72ae776ad38ef70b809046b7e26930c1","sha256:3a69426b339cd10595c4c3c080bc6f0d9c2ba0526c24f4eb632c97e6012d3593"],"state_sha256":"02b39a273429f0ef03ffbd6a5e55d53304205089a5d9b6cd85578e2572ca46bc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HIz7H0R/zjKD7t7tAsFExc7V2M7Q1hWr1zVundUQbembPHMuTtnOlk6HJtRETwwBUA1h+R525coF7KxkO0IQBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T11:45:50.291432Z","bundle_sha256":"fa062d3d3044b86656af361efaf5673fc4e30ea5cecde952bcb6ef8cf9f339ce"}}