{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:XNLLCMDOD6WLCUOOCK3NALBOFS","short_pith_number":"pith:XNLLCMDO","canonical_record":{"source":{"id":"1901.05515","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-16T20:08:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dd01de7d281539f75308bd570b0b40708176a996d50de8499357acfa9c68fe9a","abstract_canon_sha256":"2581c6f06605770affd53b468981277acbee7306e4003b970b9a1b6710538ffe"},"schema_version":"1.0"},"canonical_sha256":"bb56b1306e1facb151ce12b6d02c2e2cb5d05182e6f90e93c0c1497acd845709","source":{"kind":"arxiv","id":"1901.05515","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.05515","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"arxiv_version","alias_value":"1901.05515v2","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05515","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"pith_short_12","alias_value":"XNLLCMDOD6WL","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XNLLCMDOD6WLCUOO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XNLLCMDO","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:XNLLCMDOD6WLCUOOCK3NALBOFS","target":"record","payload":{"canonical_record":{"source":{"id":"1901.05515","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-16T20:08:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dd01de7d281539f75308bd570b0b40708176a996d50de8499357acfa9c68fe9a","abstract_canon_sha256":"2581c6f06605770affd53b468981277acbee7306e4003b970b9a1b6710538ffe"},"schema_version":"1.0"},"canonical_sha256":"bb56b1306e1facb151ce12b6d02c2e2cb5d05182e6f90e93c0c1497acd845709","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:18.837865Z","signature_b64":"iAeqRuy9A3509UfcCJYi13/1L3330hvjJlznb1zAd+IndVfqtWVRSIGjZtn7KupP6PaAOm7gFoXeoK8aHv0wAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb56b1306e1facb151ce12b6d02c2e2cb5d05182e6f90e93c0c1497acd845709","last_reissued_at":"2026-05-17T23:46:18.837188Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:18.837188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.05515","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":"2026-05-17T23:46:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbnNmCFhnOE8qV0VLRI1h0brZdWOlM3oALBSSvek08VKnENHsRf5C4cB3ZIxrRaLGJUpGMnINSRgQTj6aFM9DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T15:21:48.501744Z"},"content_sha256":"52a3611da6cc31c34b9c23d0e6ddab3aa75468065439e6a45a75179df5d9439c","schema_version":"1.0","event_id":"sha256:52a3611da6cc31c34b9c23d0e6ddab3aa75468065439e6a45a75179df5d9439c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:XNLLCMDOD6WLCUOOCK3NALBOFS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The information-theoretic value of unlabeled data in semi-supervised learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Golovnev, Bal\\'azs Sz\\\"or\\'enyi, D\\'avid P\\'al","submitted_at":"2019-01-16T20:08:01Z","abstract_excerpt":"We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $\\Theta(\\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size.\n  Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the num"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05515","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":""},"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":"2026-05-17T23:46:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C9O5st5th588wOz/XZoFPGEPiCjJ/SmqV4zrBXZSa8XUOm7I3jCKBbgEQX8iiAOCY4brJrpG+fhIeMMwtt6NAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T15:21:48.502118Z"},"content_sha256":"901d161cd78efd31946bffcc923d0635f3d5b3bf8ca60e0879e49aefd95cc9cb","schema_version":"1.0","event_id":"sha256:901d161cd78efd31946bffcc923d0635f3d5b3bf8ca60e0879e49aefd95cc9cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/bundle.json","state_url":"https://pith.science/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/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":"2026-05-19T15:21:48Z","links":{"resolver":"https://pith.science/pith/XNLLCMDOD6WLCUOOCK3NALBOFS","bundle":"https://pith.science/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/bundle.json","state":"https://pith.science/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XNLLCMDOD6WLCUOOCK3NALBOFS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:XNLLCMDOD6WLCUOOCK3NALBOFS","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":"2581c6f06605770affd53b468981277acbee7306e4003b970b9a1b6710538ffe","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-16T20:08:01Z","title_canon_sha256":"dd01de7d281539f75308bd570b0b40708176a996d50de8499357acfa9c68fe9a"},"schema_version":"1.0","source":{"id":"1901.05515","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.05515","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"arxiv_version","alias_value":"1901.05515v2","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05515","created_at":"2026-05-17T23:46:18Z"},{"alias_kind":"pith_short_12","alias_value":"XNLLCMDOD6WL","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XNLLCMDOD6WLCUOO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XNLLCMDO","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:901d161cd78efd31946bffcc923d0635f3d5b3bf8ca60e0879e49aefd95cc9cb","target":"graph","created_at":"2026-05-17T23:46:18Z","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 quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $\\Theta(\\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size.\n  Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the num","authors_text":"Alexander Golovnev, Bal\\'azs Sz\\\"or\\'enyi, D\\'avid P\\'al","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-16T20:08:01Z","title":"The information-theoretic value of unlabeled data in semi-supervised learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05515","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":"sha256:52a3611da6cc31c34b9c23d0e6ddab3aa75468065439e6a45a75179df5d9439c","target":"record","created_at":"2026-05-17T23:46:18Z","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":"2581c6f06605770affd53b468981277acbee7306e4003b970b9a1b6710538ffe","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-16T20:08:01Z","title_canon_sha256":"dd01de7d281539f75308bd570b0b40708176a996d50de8499357acfa9c68fe9a"},"schema_version":"1.0","source":{"id":"1901.05515","kind":"arxiv","version":2}},"canonical_sha256":"bb56b1306e1facb151ce12b6d02c2e2cb5d05182e6f90e93c0c1497acd845709","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb56b1306e1facb151ce12b6d02c2e2cb5d05182e6f90e93c0c1497acd845709","first_computed_at":"2026-05-17T23:46:18.837188Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:18.837188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iAeqRuy9A3509UfcCJYi13/1L3330hvjJlznb1zAd+IndVfqtWVRSIGjZtn7KupP6PaAOm7gFoXeoK8aHv0wAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:18.837865Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.05515","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:52a3611da6cc31c34b9c23d0e6ddab3aa75468065439e6a45a75179df5d9439c","sha256:901d161cd78efd31946bffcc923d0635f3d5b3bf8ca60e0879e49aefd95cc9cb"],"state_sha256":"2ded72b93e026569dde13995e1b1b8994f2f1e5babe8005a1f92870de47e6df3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1K08bcsUSJNAS8tzCCukQgRJNsWOkRQKu9uub6f5zqvNm9rtp5zfXiQG9IPsZ/7IXFWBPlv32nJvrJIpAWfWDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T15:21:48.508209Z","bundle_sha256":"b650b9320c6fce81f68e40de3c9457274510cd0478b557181660f2c90e4f9373"}}