{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:RHBU45JINZXHHGKA72VE6NTLOY","short_pith_number":"pith:RHBU45JI","canonical_record":{"source":{"id":"1903.01432","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-04T18:55:09Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"43b2f8e1ac86b53a0c539be0586a7a76e211bcb31a234413eb08cbb2420b1946","abstract_canon_sha256":"4e5b94f61e567c8f171e1ca3924c12dd7af10f589f3fa793113b6ff1fb328571"},"schema_version":"1.0"},"canonical_sha256":"89c34e75286e6e739940feaa4f366b76036220be764312b344b14f93a37247d1","source":{"kind":"arxiv","id":"1903.01432","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.01432","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"arxiv_version","alias_value":"1903.01432v2","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01432","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"pith_short_12","alias_value":"RHBU45JINZXH","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RHBU45JINZXHHGKA","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RHBU45JI","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:RHBU45JINZXHHGKA72VE6NTLOY","target":"record","payload":{"canonical_record":{"source":{"id":"1903.01432","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-04T18:55:09Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"43b2f8e1ac86b53a0c539be0586a7a76e211bcb31a234413eb08cbb2420b1946","abstract_canon_sha256":"4e5b94f61e567c8f171e1ca3924c12dd7af10f589f3fa793113b6ff1fb328571"},"schema_version":"1.0"},"canonical_sha256":"89c34e75286e6e739940feaa4f366b76036220be764312b344b14f93a37247d1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:00.555230Z","signature_b64":"xv1Tws8j7Sbg9CXd5chyXghypn0rtgjn4cVJJnA6Yc5Z9lekj2bbbU6Y3n8WQ6G6zz4KgZvHtj4GmAc5RI0mBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89c34e75286e6e739940feaa4f366b76036220be764312b344b14f93a37247d1","last_reissued_at":"2026-05-17T23:52:00.554695Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:00.554695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.01432","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":"2026-05-17T23:52:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4R1fECviKCsxz8VYvc9iYxYzvhNoDbh7KiI+JWjkqRbibflSiHXcJAAgadbFfn5QmIAzU16qEej5BB5/tr4yCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:47:29.388065Z"},"content_sha256":"0088760396e712e299a52b1d918b2867aad912e0cd254b6e4e25ec4a84f21c98","schema_version":"1.0","event_id":"sha256:0088760396e712e299a52b1d918b2867aad912e0cd254b6e4e25ec4a84f21c98"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:RHBU45JINZXHHGKA72VE6NTLOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data Amplification: Instance-Optimal Property Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Alon Orlitsky, Yi Hao","submitted_at":"2019-03-04T18:55:09Z","abstract_excerpt":"The best-known and most commonly used distribution-property estimation technique uses a plug-in estimator, with empirical frequency replacing the underlying distribution. We present novel linear-time-computable estimators that significantly \"amplify\" the effective amount of data available. For a large variety of distribution properties including four of the most popular ones and for every underlying distribution, they achieve the accuracy that the empirical-frequency plug-in estimators would attain using a logarithmic-factor more samples.\n  Specifically, for Shannon entropy and a very broad cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01432","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":"2026-05-17T23:52:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5kAPpf9ahAFhcJ3mWtC4Sca39G+LkTmBPM9UaLChUps0Y+Mh2ROxopoSCFhe+zwGyOGgkT2W9cUiX0vhzMaKCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T20:47:29.388647Z"},"content_sha256":"125525b2b52b25081bf5796ca03f1d80bf25021767713daccb123423c1a3218c","schema_version":"1.0","event_id":"sha256:125525b2b52b25081bf5796ca03f1d80bf25021767713daccb123423c1a3218c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RHBU45JINZXHHGKA72VE6NTLOY/bundle.json","state_url":"https://pith.science/pith/RHBU45JINZXHHGKA72VE6NTLOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RHBU45JINZXHHGKA72VE6NTLOY/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":"2026-06-05T20:47:29Z","links":{"resolver":"https://pith.science/pith/RHBU45JINZXHHGKA72VE6NTLOY","bundle":"https://pith.science/pith/RHBU45JINZXHHGKA72VE6NTLOY/bundle.json","state":"https://pith.science/pith/RHBU45JINZXHHGKA72VE6NTLOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RHBU45JINZXHHGKA72VE6NTLOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:RHBU45JINZXHHGKA72VE6NTLOY","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":"4e5b94f61e567c8f171e1ca3924c12dd7af10f589f3fa793113b6ff1fb328571","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-04T18:55:09Z","title_canon_sha256":"43b2f8e1ac86b53a0c539be0586a7a76e211bcb31a234413eb08cbb2420b1946"},"schema_version":"1.0","source":{"id":"1903.01432","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.01432","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"arxiv_version","alias_value":"1903.01432v2","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.01432","created_at":"2026-05-17T23:52:00Z"},{"alias_kind":"pith_short_12","alias_value":"RHBU45JINZXH","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"RHBU45JINZXHHGKA","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"RHBU45JI","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:125525b2b52b25081bf5796ca03f1d80bf25021767713daccb123423c1a3218c","target":"graph","created_at":"2026-05-17T23:52:00Z","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":"The best-known and most commonly used distribution-property estimation technique uses a plug-in estimator, with empirical frequency replacing the underlying distribution. We present novel linear-time-computable estimators that significantly \"amplify\" the effective amount of data available. For a large variety of distribution properties including four of the most popular ones and for every underlying distribution, they achieve the accuracy that the empirical-frequency plug-in estimators would attain using a logarithmic-factor more samples.\n  Specifically, for Shannon entropy and a very broad cl","authors_text":"Alon Orlitsky, Yi Hao","cross_cats":["cs.LG","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-04T18:55:09Z","title":"Data Amplification: Instance-Optimal Property Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.01432","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":"sha256:0088760396e712e299a52b1d918b2867aad912e0cd254b6e4e25ec4a84f21c98","target":"record","created_at":"2026-05-17T23:52:00Z","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":"4e5b94f61e567c8f171e1ca3924c12dd7af10f589f3fa793113b6ff1fb328571","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-04T18:55:09Z","title_canon_sha256":"43b2f8e1ac86b53a0c539be0586a7a76e211bcb31a234413eb08cbb2420b1946"},"schema_version":"1.0","source":{"id":"1903.01432","kind":"arxiv","version":2}},"canonical_sha256":"89c34e75286e6e739940feaa4f366b76036220be764312b344b14f93a37247d1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89c34e75286e6e739940feaa4f366b76036220be764312b344b14f93a37247d1","first_computed_at":"2026-05-17T23:52:00.554695Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:00.554695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xv1Tws8j7Sbg9CXd5chyXghypn0rtgjn4cVJJnA6Yc5Z9lekj2bbbU6Y3n8WQ6G6zz4KgZvHtj4GmAc5RI0mBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:00.555230Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.01432","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0088760396e712e299a52b1d918b2867aad912e0cd254b6e4e25ec4a84f21c98","sha256:125525b2b52b25081bf5796ca03f1d80bf25021767713daccb123423c1a3218c"],"state_sha256":"2a3a5c5200517f5afcab4b88f75b62c9fed2ed6fac9bd2bcec0d716c45cd5d53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/K3Kp5GJuRYS87JL0gZYhgfbP4Oxt44dZ9+Zk6f0wX27YvwaXIatjkl/NuO+Eh7SpJebyXKRO+X0wssVTP07CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T20:47:29.392536Z","bundle_sha256":"837ca0ab10abe9577ef35ef2e467780d16903c10b2299e6aeabd6c9f0681edfa"}}