{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NES2B3VUHJTJEF54QCUGCWPUVB","short_pith_number":"pith:NES2B3VU","canonical_record":{"source":{"id":"1907.06558","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-15T15:56:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"18c1c81cc54234affef9b431bd662bd1d1444869598b3982db5d5121fda16ab0","abstract_canon_sha256":"1c2980b468d1eea112dd159bf28f8421a2c1941a7c00c521c7df64b0901c6b80"},"schema_version":"1.0"},"canonical_sha256":"6925a0eeb43a669217bc80a86159f4a842c27b88ec56c9bcc54ef4f66e159f6f","source":{"kind":"arxiv","id":"1907.06558","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06558","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06558v2","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06558","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"NES2B3VUHJTJ","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"NES2B3VUHJTJEF54","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"NES2B3VU","created_at":"2026-07-05T02:34:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NES2B3VUHJTJEF54QCUGCWPUVB","target":"record","payload":{"canonical_record":{"source":{"id":"1907.06558","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-15T15:56:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"18c1c81cc54234affef9b431bd662bd1d1444869598b3982db5d5121fda16ab0","abstract_canon_sha256":"1c2980b468d1eea112dd159bf28f8421a2c1941a7c00c521c7df64b0901c6b80"},"schema_version":"1.0"},"canonical_sha256":"6925a0eeb43a669217bc80a86159f4a842c27b88ec56c9bcc54ef4f66e159f6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:34:37.134946Z","signature_b64":"eel4SqDUXvaA6IOvvBg5hfshYU9lTuAmq0nAZP8LfdYXHYi7RxzGmat9233L92XQFMGh8r4ThCGymgkfWdgJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6925a0eeb43a669217bc80a86159f4a842c27b88ec56c9bcc54ef4f66e159f6f","last_reissued_at":"2026-07-05T02:34:37.134498Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:34:37.134498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.06558","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":"2026-07-05T02:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0iD+i1EzKvAvylCwPc+X+r1M1v5FVERMGZrTwLW0mm6HOsaGiIoLg5cFfZ/TkFnjeWOzR0kRcC7glMMB/PYlAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:50:06.880486Z"},"content_sha256":"6983c0cd326941f4a22c92d954cbd52a705925b835344ccfded1445bb8a3ddd9","schema_version":"1.0","event_id":"sha256:6983c0cd326941f4a22c92d954cbd52a705925b835344ccfded1445bb8a3ddd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NES2B3VUHJTJEF54QCUGCWPUVB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alykhan Tejani, Deepak Dilipkumar, Ferenc Huszar, Lucas Theis, Pranay Kumar Myana, Sofia Ira Ktena, Steven Yoo, Wenzhe Shi","submitted_at":"2019-07-15T15:56:49Z","abstract_excerpt":"One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad campaigns and other factors. The predominant strategy to keep up with these shifts is to train predictive models continuously, on fresh data, in order to prevent them from becoming stale. However, in many ad systems positive labels are only observed after a possibly long and random delay. These delayed labels pose a challenge to data freshness in continuous training: fresh data may not have complete label information"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06558","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1907.06558/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:34:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X4alJjIJ/GmjC6WG2NbVP0S+3oKJH98sjonK/wy1ZprSgP3q2QHsNnOhVxFCt1ePuizv79A9CofYS2nBXq45Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T15:50:06.881390Z"},"content_sha256":"293d240f9e92ff40e333e5f59e3a05a811cf4ff3d8c3f782b0965ddeeebd635b","schema_version":"1.0","event_id":"sha256:293d240f9e92ff40e333e5f59e3a05a811cf4ff3d8c3f782b0965ddeeebd635b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NES2B3VUHJTJEF54QCUGCWPUVB/bundle.json","state_url":"https://pith.science/pith/NES2B3VUHJTJEF54QCUGCWPUVB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NES2B3VUHJTJEF54QCUGCWPUVB/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":"2026-08-08T15:50:06Z","links":{"resolver":"https://pith.science/pith/NES2B3VUHJTJEF54QCUGCWPUVB","bundle":"https://pith.science/pith/NES2B3VUHJTJEF54QCUGCWPUVB/bundle.json","state":"https://pith.science/pith/NES2B3VUHJTJEF54QCUGCWPUVB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NES2B3VUHJTJEF54QCUGCWPUVB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NES2B3VUHJTJEF54QCUGCWPUVB","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":"1c2980b468d1eea112dd159bf28f8421a2c1941a7c00c521c7df64b0901c6b80","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-15T15:56:49Z","title_canon_sha256":"18c1c81cc54234affef9b431bd662bd1d1444869598b3982db5d5121fda16ab0"},"schema_version":"1.0","source":{"id":"1907.06558","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.06558","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"arxiv_version","alias_value":"1907.06558v2","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06558","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_12","alias_value":"NES2B3VUHJTJ","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_16","alias_value":"NES2B3VUHJTJEF54","created_at":"2026-07-05T02:34:37Z"},{"alias_kind":"pith_short_8","alias_value":"NES2B3VU","created_at":"2026-07-05T02:34:37Z"}],"graph_snapshots":[{"event_id":"sha256:293d240f9e92ff40e333e5f59e3a05a811cf4ff3d8c3f782b0965ddeeebd635b","target":"graph","created_at":"2026-07-05T02:34:37Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1907.06558/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad campaigns and other factors. The predominant strategy to keep up with these shifts is to train predictive models continuously, on fresh data, in order to prevent them from becoming stale. However, in many ad systems positive labels are only observed after a possibly long and random delay. These delayed labels pose a challenge to data freshness in continuous training: fresh data may not have complete label information","authors_text":"Alykhan Tejani, Deepak Dilipkumar, Ferenc Huszar, Lucas Theis, Pranay Kumar Myana, Sofia Ira Ktena, Steven Yoo, Wenzhe Shi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-15T15:56:49Z","title":"Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06558","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":"sha256:6983c0cd326941f4a22c92d954cbd52a705925b835344ccfded1445bb8a3ddd9","target":"record","created_at":"2026-07-05T02:34:37Z","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":"1c2980b468d1eea112dd159bf28f8421a2c1941a7c00c521c7df64b0901c6b80","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-15T15:56:49Z","title_canon_sha256":"18c1c81cc54234affef9b431bd662bd1d1444869598b3982db5d5121fda16ab0"},"schema_version":"1.0","source":{"id":"1907.06558","kind":"arxiv","version":2}},"canonical_sha256":"6925a0eeb43a669217bc80a86159f4a842c27b88ec56c9bcc54ef4f66e159f6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6925a0eeb43a669217bc80a86159f4a842c27b88ec56c9bcc54ef4f66e159f6f","first_computed_at":"2026-07-05T02:34:37.134498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:34:37.134498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eel4SqDUXvaA6IOvvBg5hfshYU9lTuAmq0nAZP8LfdYXHYi7RxzGmat9233L92XQFMGh8r4ThCGymgkfWdgJBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:34:37.134946Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.06558","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6983c0cd326941f4a22c92d954cbd52a705925b835344ccfded1445bb8a3ddd9","sha256:293d240f9e92ff40e333e5f59e3a05a811cf4ff3d8c3f782b0965ddeeebd635b"],"state_sha256":"0beda70f73dad5db288847777f821a3c26802df22e77d4f9da64805721a5c728"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TK2qdyP44wvvdI60jAHPL0evg1HZdtkQepmx7t8yCJbloz8dLYzrR9EL9j/CQUmJ/JCa9PIu+vYhaye+e9VABA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T15:50:06.887619Z","bundle_sha256":"c6bea9c7572efe671614922a246f0ea2d2b260ff911283b11149951c97fcd665"}}