{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:PTZPKDKPETEEAWURDT7K6RBFBV","short_pith_number":"pith:PTZPKDKP","canonical_record":{"source":{"id":"1504.06681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-25T04:41:30Z","cross_cats_sorted":[],"title_canon_sha256":"59fcdd4605256e05d311c2aa2a65ca1607af34e5c54159f7cb421fa5c51e83b5","abstract_canon_sha256":"b61620233c80464fcb5ed075edb028283728ecf1b1d7a12632aac9ebd14b7244"},"schema_version":"1.0"},"canonical_sha256":"7cf2f50d4f24c8405a911cfeaf44250d6e517ff23a01f1dee2a74ee90a560730","source":{"kind":"arxiv","id":"1504.06681","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.06681","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"arxiv_version","alias_value":"1504.06681v1","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.06681","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"pith_short_12","alias_value":"PTZPKDKPETEE","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_16","alias_value":"PTZPKDKPETEEAWUR","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_8","alias_value":"PTZPKDKP","created_at":"2026-05-18T12:29:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:PTZPKDKPETEEAWURDT7K6RBFBV","target":"record","payload":{"canonical_record":{"source":{"id":"1504.06681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-25T04:41:30Z","cross_cats_sorted":[],"title_canon_sha256":"59fcdd4605256e05d311c2aa2a65ca1607af34e5c54159f7cb421fa5c51e83b5","abstract_canon_sha256":"b61620233c80464fcb5ed075edb028283728ecf1b1d7a12632aac9ebd14b7244"},"schema_version":"1.0"},"canonical_sha256":"7cf2f50d4f24c8405a911cfeaf44250d6e517ff23a01f1dee2a74ee90a560730","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:52.784259Z","signature_b64":"0ofsK1juDEKRhMB11oOAmfiAYoqHcOxnPrI+m8zLja1ZMEn+oMArBX3d2xt3NTg/gPLouyu9P/MuWf6/UzpkDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cf2f50d4f24c8405a911cfeaf44250d6e517ff23a01f1dee2a74ee90a560730","last_reissued_at":"2026-05-18T02:17:52.783638Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:52.783638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.06681","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":"2026-05-18T02:17:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6eAEwInwI7h/Sr2kIs5uUJ4YMBHYH9+T53seUrWyaAnLoTxUiYV7TCSznVjWVfxALaiSY8vWL7owMweS7JZ/BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:13:47.674615Z"},"content_sha256":"8a1f3457bac2604789fe737c818f687e8b584145200963ca519705ad8efabd07","schema_version":"1.0","event_id":"sha256:8a1f3457bac2604789fe737c818f687e8b584145200963ca519705ad8efabd07"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:PTZPKDKPETEEAWURDT7K6RBFBV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Convex Optimization Using Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam Wierman, Anish Agarwal, Lachlan L. H. Andrew, Niangjun Chen, Siddharth Barman","submitted_at":"2015-04-25T04:41:30Z","abstract_excerpt":"Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.06681","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":"2026-05-18T02:17:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NaYduGW0MBhsYz4VB2xk/YluHUAMOac2f9az4gKUDREgvyUXRUTHlSIV3dsfm0O/BFGZUMZ6OV0ifj6DBQ+bAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T19:13:47.675256Z"},"content_sha256":"52831b7d3d4f8fc8d0bdc00a4f06ca431ad07fde16d934ef1dc2f2741f77c50a","schema_version":"1.0","event_id":"sha256:52831b7d3d4f8fc8d0bdc00a4f06ca431ad07fde16d934ef1dc2f2741f77c50a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PTZPKDKPETEEAWURDT7K6RBFBV/bundle.json","state_url":"https://pith.science/pith/PTZPKDKPETEEAWURDT7K6RBFBV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PTZPKDKPETEEAWURDT7K6RBFBV/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":"2026-06-08T19:13:47Z","links":{"resolver":"https://pith.science/pith/PTZPKDKPETEEAWURDT7K6RBFBV","bundle":"https://pith.science/pith/PTZPKDKPETEEAWURDT7K6RBFBV/bundle.json","state":"https://pith.science/pith/PTZPKDKPETEEAWURDT7K6RBFBV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PTZPKDKPETEEAWURDT7K6RBFBV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:PTZPKDKPETEEAWURDT7K6RBFBV","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":"b61620233c80464fcb5ed075edb028283728ecf1b1d7a12632aac9ebd14b7244","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-25T04:41:30Z","title_canon_sha256":"59fcdd4605256e05d311c2aa2a65ca1607af34e5c54159f7cb421fa5c51e83b5"},"schema_version":"1.0","source":{"id":"1504.06681","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.06681","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"arxiv_version","alias_value":"1504.06681v1","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.06681","created_at":"2026-05-18T02:17:52Z"},{"alias_kind":"pith_short_12","alias_value":"PTZPKDKPETEE","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_16","alias_value":"PTZPKDKPETEEAWUR","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_8","alias_value":"PTZPKDKP","created_at":"2026-05-18T12:29:37Z"}],"graph_snapshots":[{"event_id":"sha256:52831b7d3d4f8fc8d0bdc00a4f06ca431ad07fde16d934ef1dc2f2741f77c50a","target":"graph","created_at":"2026-05-18T02:17:52Z","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":"Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial ","authors_text":"Adam Wierman, Anish Agarwal, Lachlan L. H. Andrew, Niangjun Chen, Siddharth Barman","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-25T04:41:30Z","title":"Online Convex Optimization Using Predictions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.06681","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":"sha256:8a1f3457bac2604789fe737c818f687e8b584145200963ca519705ad8efabd07","target":"record","created_at":"2026-05-18T02:17:52Z","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":"b61620233c80464fcb5ed075edb028283728ecf1b1d7a12632aac9ebd14b7244","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-25T04:41:30Z","title_canon_sha256":"59fcdd4605256e05d311c2aa2a65ca1607af34e5c54159f7cb421fa5c51e83b5"},"schema_version":"1.0","source":{"id":"1504.06681","kind":"arxiv","version":1}},"canonical_sha256":"7cf2f50d4f24c8405a911cfeaf44250d6e517ff23a01f1dee2a74ee90a560730","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7cf2f50d4f24c8405a911cfeaf44250d6e517ff23a01f1dee2a74ee90a560730","first_computed_at":"2026-05-18T02:17:52.783638Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:17:52.783638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0ofsK1juDEKRhMB11oOAmfiAYoqHcOxnPrI+m8zLja1ZMEn+oMArBX3d2xt3NTg/gPLouyu9P/MuWf6/UzpkDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:17:52.784259Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.06681","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8a1f3457bac2604789fe737c818f687e8b584145200963ca519705ad8efabd07","sha256:52831b7d3d4f8fc8d0bdc00a4f06ca431ad07fde16d934ef1dc2f2741f77c50a"],"state_sha256":"fd7042fc406fc3da5c182b1c11695ac3559e879604bd4d796caa180928005d4d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pGcZuVSjAmVKBROmEWjBNpvZXPQXmNOpo79itX5v7Ruehw+OHxcpZiMKLhm9sGI/D0MMWztqdc0ibYG1NYRnBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T19:13:47.678386Z","bundle_sha256":"c2eabd1e7116e4d27213a169033aef91cec04dd88c035137e157ea019efcf5ab"}}