{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:O5Q4YT2NULIZLL32SS7X3WIDRC","short_pith_number":"pith:O5Q4YT2N","canonical_record":{"source":{"id":"1810.00846","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-01T17:38:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4dc8ad93023dc21cced6a494795dfc1044c973b74015da4b685eaf2e66a9584a","abstract_canon_sha256":"33a41ddad613dcf7aa6746988da6db348e59c1d1d5b3d442d9b206cd39f09942"},"schema_version":"1.0"},"canonical_sha256":"7761cc4f4da2d195af7a94bf7dd903888565dc8a30dbc6cd17120b9b68dc616f","source":{"kind":"arxiv","id":"1810.00846","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00846","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00846v2","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00846","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"pith_short_12","alias_value":"O5Q4YT2NULIZ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O5Q4YT2NULIZLL32","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O5Q4YT2N","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:O5Q4YT2NULIZLL32SS7X3WIDRC","target":"record","payload":{"canonical_record":{"source":{"id":"1810.00846","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-01T17:38:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4dc8ad93023dc21cced6a494795dfc1044c973b74015da4b685eaf2e66a9584a","abstract_canon_sha256":"33a41ddad613dcf7aa6746988da6db348e59c1d1d5b3d442d9b206cd39f09942"},"schema_version":"1.0"},"canonical_sha256":"7761cc4f4da2d195af7a94bf7dd903888565dc8a30dbc6cd17120b9b68dc616f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:43.650066Z","signature_b64":"NgJBWHb1nNznikodcxJuI/PrnCpPM9trpHI51tB5spsE/MYDxA4TWeYlan0YMp7oFHs/gp7PMHeVgfSum5KWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7761cc4f4da2d195af7a94bf7dd903888565dc8a30dbc6cd17120b9b68dc616f","last_reissued_at":"2026-05-17T23:40:43.649497Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:43.649497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.00846","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":"2026-05-17T23:40:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sVvEHKrR74yR8TJ2eRdhh/BPsmKbspp6rCbUg/rcs9mU6+1TSEG6Z3kRbh50z5yiVyaGfwJX4l6vwQUNqzcoBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T10:55:34.976866Z"},"content_sha256":"c8b710e2c3dcf15175d678795f29a431c4296ee909b81980c59679a0e76b6943","schema_version":"1.0","event_id":"sha256:c8b710e2c3dcf15175d678795f29a431c4296ee909b81980c59679a0e76b6943"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:O5Q4YT2NULIZLL32SS7X3WIDRC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classification from Positive, Unlabeled and Biased Negative Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gang Niu, Masashi Sugiyama, Yu-Guan Hsieh","submitted_at":"2018-10-01T17:38:58Z","abstract_excerpt":"In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in practice. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. We provide a method based on empirical risk minimization to address this PUbN classification problem. Our approach can be regarded as a novel example-weighting alg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00846","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":"2026-05-17T23:40:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ee4Hxm0G4XAniSBzwCcqdiuWImyGTX0sxdqwMnWVNlV6owewnBAyCwrKTYoHgeGaZcv5dC7SCialZE5pon0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T10:55:34.977213Z"},"content_sha256":"8bef9e1ef7530cbc621ef98b7d3ae3058ce89ec45a1f833207f1005900f28a25","schema_version":"1.0","event_id":"sha256:8bef9e1ef7530cbc621ef98b7d3ae3058ce89ec45a1f833207f1005900f28a25"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/bundle.json","state_url":"https://pith.science/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/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":"2026-06-29T10:55:34Z","links":{"resolver":"https://pith.science/pith/O5Q4YT2NULIZLL32SS7X3WIDRC","bundle":"https://pith.science/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/bundle.json","state":"https://pith.science/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5Q4YT2NULIZLL32SS7X3WIDRC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:O5Q4YT2NULIZLL32SS7X3WIDRC","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":"33a41ddad613dcf7aa6746988da6db348e59c1d1d5b3d442d9b206cd39f09942","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-01T17:38:58Z","title_canon_sha256":"4dc8ad93023dc21cced6a494795dfc1044c973b74015da4b685eaf2e66a9584a"},"schema_version":"1.0","source":{"id":"1810.00846","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00846","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00846v2","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00846","created_at":"2026-05-17T23:40:43Z"},{"alias_kind":"pith_short_12","alias_value":"O5Q4YT2NULIZ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O5Q4YT2NULIZLL32","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O5Q4YT2N","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:8bef9e1ef7530cbc621ef98b7d3ae3058ce89ec45a1f833207f1005900f28a25","target":"graph","created_at":"2026-05-17T23:40:43Z","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":"In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenarios. However, collecting a non-representative N set that contains only a small portion of all possible N data can often be much easier in practice. This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning. We provide a method based on empirical risk minimization to address this PUbN classification problem. Our approach can be regarded as a novel example-weighting alg","authors_text":"Gang Niu, Masashi Sugiyama, Yu-Guan Hsieh","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-01T17:38:58Z","title":"Classification from Positive, Unlabeled and Biased Negative Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00846","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:c8b710e2c3dcf15175d678795f29a431c4296ee909b81980c59679a0e76b6943","target":"record","created_at":"2026-05-17T23:40:43Z","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":"33a41ddad613dcf7aa6746988da6db348e59c1d1d5b3d442d9b206cd39f09942","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-01T17:38:58Z","title_canon_sha256":"4dc8ad93023dc21cced6a494795dfc1044c973b74015da4b685eaf2e66a9584a"},"schema_version":"1.0","source":{"id":"1810.00846","kind":"arxiv","version":2}},"canonical_sha256":"7761cc4f4da2d195af7a94bf7dd903888565dc8a30dbc6cd17120b9b68dc616f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7761cc4f4da2d195af7a94bf7dd903888565dc8a30dbc6cd17120b9b68dc616f","first_computed_at":"2026-05-17T23:40:43.649497Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:43.649497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NgJBWHb1nNznikodcxJuI/PrnCpPM9trpHI51tB5spsE/MYDxA4TWeYlan0YMp7oFHs/gp7PMHeVgfSum5KWDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:43.650066Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.00846","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c8b710e2c3dcf15175d678795f29a431c4296ee909b81980c59679a0e76b6943","sha256:8bef9e1ef7530cbc621ef98b7d3ae3058ce89ec45a1f833207f1005900f28a25"],"state_sha256":"74be22f66baa167b4c052647f053a5338e72fabcee200f2988271639eee76eae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x051dDdNkUZYgZR6OoRRVzd01eDAsVO5obAUP/8bCqpDED7T4mQkGJrqAUxfGR4DdVuTubhgP4STlABoeE+GCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T10:55:34.979060Z","bundle_sha256":"c18135bc6e92c2dc6e0d79555a167a856dcf0577c17e2ea1e6c37bfa05fe1154"}}