{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SQ2GFXNJRPIZTO7ZMHOAI6JFDO","short_pith_number":"pith:SQ2GFXNJ","canonical_record":{"source":{"id":"1812.09520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-22T12:24:02Z","cross_cats_sorted":["cs.LG","math.IT","stat.ML"],"title_canon_sha256":"4c9f81a66d7a4b28af7d336af7c60881e2c2e7e30506b7d5e2f2c7995e54db80","abstract_canon_sha256":"8096159ed2b9c8487761a7ea50e8f2e4d2e83fa0086c13ef419c5ebcd30eea20"},"schema_version":"1.0"},"canonical_sha256":"943462dda98bd199bbf961dc0479251b8177103495fc12d023c34b6c0e8554e6","source":{"kind":"arxiv","id":"1812.09520","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.09520","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"arxiv_version","alias_value":"1812.09520v1","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09520","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"pith_short_12","alias_value":"SQ2GFXNJRPIZ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SQ2GFXNJRPIZTO7Z","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SQ2GFXNJ","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SQ2GFXNJRPIZTO7ZMHOAI6JFDO","target":"record","payload":{"canonical_record":{"source":{"id":"1812.09520","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-22T12:24:02Z","cross_cats_sorted":["cs.LG","math.IT","stat.ML"],"title_canon_sha256":"4c9f81a66d7a4b28af7d336af7c60881e2c2e7e30506b7d5e2f2c7995e54db80","abstract_canon_sha256":"8096159ed2b9c8487761a7ea50e8f2e4d2e83fa0086c13ef419c5ebcd30eea20"},"schema_version":"1.0"},"canonical_sha256":"943462dda98bd199bbf961dc0479251b8177103495fc12d023c34b6c0e8554e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:27.693999Z","signature_b64":"qfjimr2wJ1ynUl0f5FLeDPZZK0WS2FsTyPyRpABKxNKrRwQC7blC9fACRog4PzctoBzH7U2ttRhuLu2Nub0YDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"943462dda98bd199bbf961dc0479251b8177103495fc12d023c34b6c0e8554e6","last_reissued_at":"2026-05-17T23:57:27.693457Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:27.693457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.09520","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":"2026-05-17T23:57:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jpoq7l5Qvnnl0kKBmrtUuFMYp/hl4bYPfGfn+ZbJGkcecOvt59ySmt7RWXpOhjPw1+4MVRGJJjXba1y2cSTmDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:06:50.942613Z"},"content_sha256":"65b77ec6785266a36372dddf290a8cd4f950c067646c3804229efd18608ddedc","schema_version":"1.0","event_id":"sha256:65b77ec6785266a36372dddf290a8cd4f950c067646c3804229efd18608ddedc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SQ2GFXNJRPIZTO7ZMHOAI6JFDO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Universal Supervised Learning for Individual Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT","stat.ML"],"primary_cat":"cs.IT","authors_text":"Meir Feder, Yaniv Fogel","submitted_at":"2018-12-22T12:24:02Z","abstract_excerpt":"Universal supervised learning is considered from an information theoretic point of view following the universal prediction approach, see Merhav and Feder (1998). We consider the standard supervised \"batch\" learning where prediction is done on a test sample once the entire training data is observed, and the individual setting where the features and labels, both in the training and test, are specific individual quantities. The information theoretic approach naturally uses the self-information loss or log-loss. Our results provide universal learning schemes that compete with a \"genie\" (or referen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09520","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":"2026-05-17T23:57:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7VRQfTxX/m5hK3/sV1o4z6w6DttHpumYHUNCXNMNgABRXsx/s9GHjd/g5pZhxhjIIc8HWECQl5/uHi7HXqUHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T08:06:50.942958Z"},"content_sha256":"c136487f175e6ab4b518987d42fbff9c787a2255bd33695f1cb6c405ef4c6b52","schema_version":"1.0","event_id":"sha256:c136487f175e6ab4b518987d42fbff9c787a2255bd33695f1cb6c405ef4c6b52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/bundle.json","state_url":"https://pith.science/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/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":"2026-05-28T08:06:50Z","links":{"resolver":"https://pith.science/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO","bundle":"https://pith.science/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/bundle.json","state":"https://pith.science/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SQ2GFXNJRPIZTO7ZMHOAI6JFDO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SQ2GFXNJRPIZTO7ZMHOAI6JFDO","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":"8096159ed2b9c8487761a7ea50e8f2e4d2e83fa0086c13ef419c5ebcd30eea20","cross_cats_sorted":["cs.LG","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-22T12:24:02Z","title_canon_sha256":"4c9f81a66d7a4b28af7d336af7c60881e2c2e7e30506b7d5e2f2c7995e54db80"},"schema_version":"1.0","source":{"id":"1812.09520","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.09520","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"arxiv_version","alias_value":"1812.09520v1","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09520","created_at":"2026-05-17T23:57:27Z"},{"alias_kind":"pith_short_12","alias_value":"SQ2GFXNJRPIZ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SQ2GFXNJRPIZTO7Z","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SQ2GFXNJ","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:c136487f175e6ab4b518987d42fbff9c787a2255bd33695f1cb6c405ef4c6b52","target":"graph","created_at":"2026-05-17T23:57:27Z","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":"Universal supervised learning is considered from an information theoretic point of view following the universal prediction approach, see Merhav and Feder (1998). We consider the standard supervised \"batch\" learning where prediction is done on a test sample once the entire training data is observed, and the individual setting where the features and labels, both in the training and test, are specific individual quantities. The information theoretic approach naturally uses the self-information loss or log-loss. Our results provide universal learning schemes that compete with a \"genie\" (or referen","authors_text":"Meir Feder, Yaniv Fogel","cross_cats":["cs.LG","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-22T12:24:02Z","title":"Universal Supervised Learning for Individual Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09520","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":"sha256:65b77ec6785266a36372dddf290a8cd4f950c067646c3804229efd18608ddedc","target":"record","created_at":"2026-05-17T23:57:27Z","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":"8096159ed2b9c8487761a7ea50e8f2e4d2e83fa0086c13ef419c5ebcd30eea20","cross_cats_sorted":["cs.LG","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-22T12:24:02Z","title_canon_sha256":"4c9f81a66d7a4b28af7d336af7c60881e2c2e7e30506b7d5e2f2c7995e54db80"},"schema_version":"1.0","source":{"id":"1812.09520","kind":"arxiv","version":1}},"canonical_sha256":"943462dda98bd199bbf961dc0479251b8177103495fc12d023c34b6c0e8554e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"943462dda98bd199bbf961dc0479251b8177103495fc12d023c34b6c0e8554e6","first_computed_at":"2026-05-17T23:57:27.693457Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:27.693457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qfjimr2wJ1ynUl0f5FLeDPZZK0WS2FsTyPyRpABKxNKrRwQC7blC9fACRog4PzctoBzH7U2ttRhuLu2Nub0YDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:27.693999Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.09520","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65b77ec6785266a36372dddf290a8cd4f950c067646c3804229efd18608ddedc","sha256:c136487f175e6ab4b518987d42fbff9c787a2255bd33695f1cb6c405ef4c6b52"],"state_sha256":"8fa24c8821445e87a5261685ef82b1e1ec6b0a6a474f28185c9334a535ae4974"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"veQ5XEcM/8TlZcFUbqBQTVl4ArI9oa6VPWo6/xHzu57aZdQU5iLeBKwNGyNxnkf7HrOOnVkkyXi9+u9eS/7xBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T08:06:50.944887Z","bundle_sha256":"3736d9de5343f655ae8ae73805d2311311d5d5a27b715ff28a0853abf9845622"}}