{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:KWW44NPG5ZDI2SVL6RA2MU65XE","short_pith_number":"pith:KWW44NPG","canonical_record":{"source":{"id":"1604.05280","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-18T19:04:59Z","cross_cats_sorted":["cs.AI","math.PR"],"title_canon_sha256":"30e11f7e82509eebf4e523025ca08165aa5e3d134b1f67b1f25697be4b903b47","abstract_canon_sha256":"d74bd10f16d3b7825a39c3dda9b549d43a8b3595e346a57ee8c221a13f729772"},"schema_version":"1.0"},"canonical_sha256":"55adce35e6ee468d4aabf441a653ddb923048af19e3a5de30c150a279a4bf9cf","source":{"kind":"arxiv","id":"1604.05280","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.05280","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"1604.05280v4","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.05280","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"KWW44NPG5ZDI","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"KWW44NPG5ZDI2SVL","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"KWW44NPG","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:KWW44NPG5ZDI2SVL6RA2MU65XE","target":"record","payload":{"canonical_record":{"source":{"id":"1604.05280","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-18T19:04:59Z","cross_cats_sorted":["cs.AI","math.PR"],"title_canon_sha256":"30e11f7e82509eebf4e523025ca08165aa5e3d134b1f67b1f25697be4b903b47","abstract_canon_sha256":"d74bd10f16d3b7825a39c3dda9b549d43a8b3595e346a57ee8c221a13f729772"},"schema_version":"1.0"},"canonical_sha256":"55adce35e6ee468d4aabf441a653ddb923048af19e3a5de30c150a279a4bf9cf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:05:12.434651Z","signature_b64":"Yl1ZJ8CHRX5BM2TSOise8np0rGE/qnDGYMq+dZSoLUGZTeQtEvaqVVBPjj/S0s4pJHkA6/p7/1Z8D7lhItNTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55adce35e6ee468d4aabf441a653ddb923048af19e3a5de30c150a279a4bf9cf","last_reissued_at":"2026-05-18T01:05:12.433966Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:05:12.433966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1604.05280","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v5m4wjGa7ynuWJNjgcJdInNkZtN+4CoZiftTHzGxj5RR4YvESIzA41ofaTEMLsEiDW++hU6T6sBb7EnXLJA4Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T02:35:40.266563Z"},"content_sha256":"80365558eefda42ee628d0c91a2a5e33d5461e7086f6541fd30fa296abf62728","schema_version":"1.0","event_id":"sha256:80365558eefda42ee628d0c91a2a5e33d5461e7086f6541fd30fa296abf62728"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:KWW44NPG5ZDI2SVL6RA2MU65XE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Asymptotic Convergence in Online Learning with Unbounded Delays","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.PR"],"primary_cat":"cs.LG","authors_text":"Jessica Taylor, Nate Soares, Scott Garrabrant","submitted_at":"2016-04-18T19:04:59Z","abstract_excerpt":"We study the problem of predicting the results of computations that are too expensive to run, via the observation of the results of smaller computations. We model this as an online learning problem with delayed feedback, where the length of the delay is unbounded, which we study mainly in a stochastic setting. We show that in this setting, consistency is not possible in general, and that optimal forecasters might not have average regret going to zero. However, it is still possible to give algorithms that converge asymptotically to Bayes-optimal predictions, by evaluating forecasters on specifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.05280","kind":"arxiv","version":4},"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":"2026-05-18T01:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WkkZEnNCi+YtM5CfjD2XV4XiCWojtXXmsNRo3WsnFrnhCVF0/35y1tUHI0gB2+hyT0Z7xDLZpE9kY5jM35IEBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T02:35:40.266915Z"},"content_sha256":"672326100ab7db0fb959fc11d58c493c78d90099a33f9733cfebc4825c4bb8db","schema_version":"1.0","event_id":"sha256:672326100ab7db0fb959fc11d58c493c78d90099a33f9733cfebc4825c4bb8db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/bundle.json","state_url":"https://pith.science/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/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":"2026-06-04T02:35:40Z","links":{"resolver":"https://pith.science/pith/KWW44NPG5ZDI2SVL6RA2MU65XE","bundle":"https://pith.science/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/bundle.json","state":"https://pith.science/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KWW44NPG5ZDI2SVL6RA2MU65XE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:KWW44NPG5ZDI2SVL6RA2MU65XE","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":"d74bd10f16d3b7825a39c3dda9b549d43a8b3595e346a57ee8c221a13f729772","cross_cats_sorted":["cs.AI","math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-18T19:04:59Z","title_canon_sha256":"30e11f7e82509eebf4e523025ca08165aa5e3d134b1f67b1f25697be4b903b47"},"schema_version":"1.0","source":{"id":"1604.05280","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1604.05280","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"1604.05280v4","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.05280","created_at":"2026-05-18T01:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"KWW44NPG5ZDI","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"KWW44NPG5ZDI2SVL","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"KWW44NPG","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:672326100ab7db0fb959fc11d58c493c78d90099a33f9733cfebc4825c4bb8db","target":"graph","created_at":"2026-05-18T01:05:12Z","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 the problem of predicting the results of computations that are too expensive to run, via the observation of the results of smaller computations. We model this as an online learning problem with delayed feedback, where the length of the delay is unbounded, which we study mainly in a stochastic setting. We show that in this setting, consistency is not possible in general, and that optimal forecasters might not have average regret going to zero. However, it is still possible to give algorithms that converge asymptotically to Bayes-optimal predictions, by evaluating forecasters on specifi","authors_text":"Jessica Taylor, Nate Soares, Scott Garrabrant","cross_cats":["cs.AI","math.PR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-18T19:04:59Z","title":"Asymptotic Convergence in Online Learning with Unbounded Delays"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.05280","kind":"arxiv","version":4},"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":"sha256:80365558eefda42ee628d0c91a2a5e33d5461e7086f6541fd30fa296abf62728","target":"record","created_at":"2026-05-18T01:05:12Z","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":"d74bd10f16d3b7825a39c3dda9b549d43a8b3595e346a57ee8c221a13f729772","cross_cats_sorted":["cs.AI","math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-04-18T19:04:59Z","title_canon_sha256":"30e11f7e82509eebf4e523025ca08165aa5e3d134b1f67b1f25697be4b903b47"},"schema_version":"1.0","source":{"id":"1604.05280","kind":"arxiv","version":4}},"canonical_sha256":"55adce35e6ee468d4aabf441a653ddb923048af19e3a5de30c150a279a4bf9cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"55adce35e6ee468d4aabf441a653ddb923048af19e3a5de30c150a279a4bf9cf","first_computed_at":"2026-05-18T01:05:12.433966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:05:12.433966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yl1ZJ8CHRX5BM2TSOise8np0rGE/qnDGYMq+dZSoLUGZTeQtEvaqVVBPjj/S0s4pJHkA6/p7/1Z8D7lhItNTDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:05:12.434651Z","signed_message":"canonical_sha256_bytes"},"source_id":"1604.05280","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:80365558eefda42ee628d0c91a2a5e33d5461e7086f6541fd30fa296abf62728","sha256:672326100ab7db0fb959fc11d58c493c78d90099a33f9733cfebc4825c4bb8db"],"state_sha256":"cfb154230b5d4ba503ce59f93f62b327104d2d318c73ee4bde1f1bec14abe714"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G+hqFQopTVi1lvFlK1GXmQa8RSCWVMXGSTY+Nia7EqnnMsHmHQmWUTrYV++hPZG5MH3P1OWEjVKmXPugJu+7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T02:35:40.268942Z","bundle_sha256":"d9851193514eb92f8cb77fcf906e851fb58f7b26fc632cf37f8871247382c699"}}