{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ABTMUZS6NTF5FK7VNWCLW4NWQR","short_pith_number":"pith:ABTMUZS6","canonical_record":{"source":{"id":"2202.04690","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-09T19:22:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"05d017994a4e3b2a4f44a217b559cd29b773ff416429a1fe32759804b2e4b24b","abstract_canon_sha256":"0c516caf666edbfe9abf25b26eb4c7055fba8420a281f66a611eb687e220b81c"},"schema_version":"1.0"},"canonical_sha256":"0066ca665e6ccbd2abf56d84bb71b684638d5667121b886f0bfe82ca6db3bbd2","source":{"kind":"arxiv","id":"2202.04690","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.04690","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"2202.04690v3","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.04690","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"ABTMUZS6NTF5","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_16","alias_value":"ABTMUZS6NTF5FK7V","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_8","alias_value":"ABTMUZS6","created_at":"2026-07-05T04:27:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ABTMUZS6NTF5FK7VNWCLW4NWQR","target":"record","payload":{"canonical_record":{"source":{"id":"2202.04690","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-09T19:22:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"05d017994a4e3b2a4f44a217b559cd29b773ff416429a1fe32759804b2e4b24b","abstract_canon_sha256":"0c516caf666edbfe9abf25b26eb4c7055fba8420a281f66a611eb687e220b81c"},"schema_version":"1.0"},"canonical_sha256":"0066ca665e6ccbd2abf56d84bb71b684638d5667121b886f0bfe82ca6db3bbd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:27:52.557023Z","signature_b64":"hUnZin/e4vyRSjA/finyKfrYPQsLXSCrGVTqU+d+xc3dgBWfSHg++iZTukJV9ZhgpC585hc+6XFJ6KvtbhqABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0066ca665e6ccbd2abf56d84bb71b684638d5667121b886f0bfe82ca6db3bbd2","last_reissued_at":"2026-07-05T04:27:52.556454Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:27:52.556454Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.04690","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"peoFdSlCV/STfiXMb9R1QSRZlmCMEiGlLFlLDVfXulXOPAWGXB9h7J84dFw7obuZ8PwmWcjOTF2T6ewtao2LAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:16:19.462304Z"},"content_sha256":"d54ac41e1101fe9b866a7507442037e0426d39522d23d4d91788a32b4d160ba4","schema_version":"1.0","event_id":"sha256:d54ac41e1101fe9b866a7507442037e0426d39522d23d4d91788a32b4d160ba4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ABTMUZS6NTF5FK7VNWCLW4NWQR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Smoothed Online Learning is as Easy as Statistical Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Adam Block, Alexander Rakhlin, Noah Golowich, Yuval Dagan","submitted_at":"2022-02-09T19:22:34Z","abstract_excerpt":"Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversarially. While the former model is often both computationally and statistically tractable, the latter requires no distributional assumptions. In an attempt to achieve the best of both worlds, previous work proposed the smooth online setting where each sample is drawn from an adversarially chosen distribution, which is smooth, i.e., it has a bounded density with respect to a fixed dominating measure. We provide tight b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.04690","kind":"arxiv","version":3},"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/2202.04690/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":"2026-07-05T04:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"doQIvZfhy+V1rWqYex2qhsurNJTsmFW1hShDSAXaAzTxTVl/q1hU14ncfbDVPN7v11KNb/Z3EVhJntVe+GYxAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:16:19.462961Z"},"content_sha256":"0a70345dcbe117c4caae2b2b9173f593ebf922a906b75d01829f58844bd615b4","schema_version":"1.0","event_id":"sha256:0a70345dcbe117c4caae2b2b9173f593ebf922a906b75d01829f58844bd615b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/bundle.json","state_url":"https://pith.science/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/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":"2026-07-07T05:16:19Z","links":{"resolver":"https://pith.science/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR","bundle":"https://pith.science/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/bundle.json","state":"https://pith.science/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ABTMUZS6NTF5FK7VNWCLW4NWQR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ABTMUZS6NTF5FK7VNWCLW4NWQR","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":"0c516caf666edbfe9abf25b26eb4c7055fba8420a281f66a611eb687e220b81c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-09T19:22:34Z","title_canon_sha256":"05d017994a4e3b2a4f44a217b559cd29b773ff416429a1fe32759804b2e4b24b"},"schema_version":"1.0","source":{"id":"2202.04690","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.04690","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"2202.04690v3","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.04690","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"ABTMUZS6NTF5","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_16","alias_value":"ABTMUZS6NTF5FK7V","created_at":"2026-07-05T04:27:52Z"},{"alias_kind":"pith_short_8","alias_value":"ABTMUZS6","created_at":"2026-07-05T04:27:52Z"}],"graph_snapshots":[{"event_id":"sha256:0a70345dcbe117c4caae2b2b9173f593ebf922a906b75d01829f58844bd615b4","target":"graph","created_at":"2026-07-05T04:27:52Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2202.04690/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Much of modern learning theory has been split between two regimes: the classical offline setting, where data arrive independently, and the online setting, where data arrive adversarially. While the former model is often both computationally and statistically tractable, the latter requires no distributional assumptions. In an attempt to achieve the best of both worlds, previous work proposed the smooth online setting where each sample is drawn from an adversarially chosen distribution, which is smooth, i.e., it has a bounded density with respect to a fixed dominating measure. We provide tight b","authors_text":"Adam Block, Alexander Rakhlin, Noah Golowich, Yuval Dagan","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-09T19:22:34Z","title":"Smoothed Online Learning is as Easy as Statistical Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.04690","kind":"arxiv","version":3},"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:d54ac41e1101fe9b866a7507442037e0426d39522d23d4d91788a32b4d160ba4","target":"record","created_at":"2026-07-05T04:27:52Z","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":"0c516caf666edbfe9abf25b26eb4c7055fba8420a281f66a611eb687e220b81c","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-09T19:22:34Z","title_canon_sha256":"05d017994a4e3b2a4f44a217b559cd29b773ff416429a1fe32759804b2e4b24b"},"schema_version":"1.0","source":{"id":"2202.04690","kind":"arxiv","version":3}},"canonical_sha256":"0066ca665e6ccbd2abf56d84bb71b684638d5667121b886f0bfe82ca6db3bbd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0066ca665e6ccbd2abf56d84bb71b684638d5667121b886f0bfe82ca6db3bbd2","first_computed_at":"2026-07-05T04:27:52.556454Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:27:52.556454Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hUnZin/e4vyRSjA/finyKfrYPQsLXSCrGVTqU+d+xc3dgBWfSHg++iZTukJV9ZhgpC585hc+6XFJ6KvtbhqABA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:27:52.557023Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.04690","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d54ac41e1101fe9b866a7507442037e0426d39522d23d4d91788a32b4d160ba4","sha256:0a70345dcbe117c4caae2b2b9173f593ebf922a906b75d01829f58844bd615b4"],"state_sha256":"b072a0112483f76e7c8a936e305ab27d592ed19af0ba6046e007b5b0594e181c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hRqalZiErxPF+vMdGKajvtIpXSXYJa/HHFStwU+sBM1RIyokphuD4B60vdIc6aofFEh/uoKQMDsZTH9NNwXIDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:16:19.467086Z","bundle_sha256":"dc5248748da3aa06ca5b3388ba76138798d9b03f226ba81384bbae9869b152b1"}}