{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BTKMGNPFEMCM4OMZMJZY34WDNM","short_pith_number":"pith:BTKMGNPF","canonical_record":{"source":{"id":"1901.11351","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T13:38:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6a62f2db0d87c75287c5cc62e813b1697dfe2a17835c4be6e38df5eb404f9bb1","abstract_canon_sha256":"3f59d30fed4daaf840d43418519a3d2339b4f07693bc19d8d5e36885a039f1c2"},"schema_version":"1.0"},"canonical_sha256":"0cd4c335e52304ce399962738df2c36b15775ea3bbff11cd8816e512bbf6765f","source":{"kind":"arxiv","id":"1901.11351","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11351","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11351v3","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11351","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_12","alias_value":"BTKMGNPFEMCM","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_16","alias_value":"BTKMGNPFEMCM4OMZ","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_8","alias_value":"BTKMGNPF","created_at":"2026-07-05T02:48:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BTKMGNPFEMCM4OMZMJZY34WDNM","target":"record","payload":{"canonical_record":{"source":{"id":"1901.11351","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T13:38:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6a62f2db0d87c75287c5cc62e813b1697dfe2a17835c4be6e38df5eb404f9bb1","abstract_canon_sha256":"3f59d30fed4daaf840d43418519a3d2339b4f07693bc19d8d5e36885a039f1c2"},"schema_version":"1.0"},"canonical_sha256":"0cd4c335e52304ce399962738df2c36b15775ea3bbff11cd8816e512bbf6765f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:48:08.838512Z","signature_b64":"29eYafcStQrKxU0UZkq3wC9rSYxLLQyrReOva/F+t5sn2Ej4nXUYv5S5B/8OG1SaQExbNIHs8iACWgNJdD5ZDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cd4c335e52304ce399962738df2c36b15775ea3bbff11cd8816e512bbf6765f","last_reissued_at":"2026-07-05T02:48:08.838063Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:48:08.838063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.11351","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":"2026-07-05T02:48:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cG7Z+wo0toXMRbj3JQt1ZGVQEMegpdICiyOhmW+0KsPfwKX3B63hi3aacWcGxJu8KEEhg/dDQc9AX0D3/JgkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:17:18.568268Z"},"content_sha256":"e585a6f5ae22d93bcc8b673df635981dd323d35baf1622b10cce799e1eb7a6ab","schema_version":"1.0","event_id":"sha256:e585a6f5ae22d93bcc8b673df635981dd323d35baf1622b10cce799e1eb7a6ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BTKMGNPFEMCM4OMZMJZY34WDNM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Issei Sato, Masashi Sugiyama, Nontawat Charoenphakdee, Taira Tsuchiya","submitted_at":"2019-01-31T13:38:00Z","abstract_excerpt":"Ordinal regression is aimed at predicting an ordinal class label. In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absolute error, mean zero-one error, and mean squared error. However, the existing studies do not take the evaluation metric into account, have a restriction on the model choice, and have no theoretical guarantee. To overcome these problems, we propose a novel generic framework for se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11351","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/1901.11351/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":"2026-07-05T02:48:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tMc78bqnb9mB2r7ToZc2nlEtOP8osemSFgqNDtp9J/dGqt4dSlXaoExPsSG0dzDNoRLnoVswlZT/uBaO4V+wBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T14:17:18.568597Z"},"content_sha256":"d7ea1887fb2a4279cf366ff1acf52b20cf7e29d4b9b47f604b177872e354a901","schema_version":"1.0","event_id":"sha256:d7ea1887fb2a4279cf366ff1acf52b20cf7e29d4b9b47f604b177872e354a901"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/bundle.json","state_url":"https://pith.science/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/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":"2026-08-06T14:17:18Z","links":{"resolver":"https://pith.science/pith/BTKMGNPFEMCM4OMZMJZY34WDNM","bundle":"https://pith.science/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/bundle.json","state":"https://pith.science/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BTKMGNPFEMCM4OMZMJZY34WDNM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BTKMGNPFEMCM4OMZMJZY34WDNM","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":"3f59d30fed4daaf840d43418519a3d2339b4f07693bc19d8d5e36885a039f1c2","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T13:38:00Z","title_canon_sha256":"6a62f2db0d87c75287c5cc62e813b1697dfe2a17835c4be6e38df5eb404f9bb1"},"schema_version":"1.0","source":{"id":"1901.11351","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11351","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11351v3","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11351","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_12","alias_value":"BTKMGNPFEMCM","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_16","alias_value":"BTKMGNPFEMCM4OMZ","created_at":"2026-07-05T02:48:08Z"},{"alias_kind":"pith_short_8","alias_value":"BTKMGNPF","created_at":"2026-07-05T02:48:08Z"}],"graph_snapshots":[{"event_id":"sha256:d7ea1887fb2a4279cf366ff1acf52b20cf7e29d4b9b47f604b177872e354a901","target":"graph","created_at":"2026-07-05T02:48:08Z","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/1901.11351/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ordinal regression is aimed at predicting an ordinal class label. In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absolute error, mean zero-one error, and mean squared error. However, the existing studies do not take the evaluation metric into account, have a restriction on the model choice, and have no theoretical guarantee. To overcome these problems, we propose a novel generic framework for se","authors_text":"Issei Sato, Masashi Sugiyama, Nontawat Charoenphakdee, Taira Tsuchiya","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T13:38:00Z","title":"Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11351","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:e585a6f5ae22d93bcc8b673df635981dd323d35baf1622b10cce799e1eb7a6ab","target":"record","created_at":"2026-07-05T02:48:08Z","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":"3f59d30fed4daaf840d43418519a3d2339b4f07693bc19d8d5e36885a039f1c2","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-31T13:38:00Z","title_canon_sha256":"6a62f2db0d87c75287c5cc62e813b1697dfe2a17835c4be6e38df5eb404f9bb1"},"schema_version":"1.0","source":{"id":"1901.11351","kind":"arxiv","version":3}},"canonical_sha256":"0cd4c335e52304ce399962738df2c36b15775ea3bbff11cd8816e512bbf6765f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cd4c335e52304ce399962738df2c36b15775ea3bbff11cd8816e512bbf6765f","first_computed_at":"2026-07-05T02:48:08.838063Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:48:08.838063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"29eYafcStQrKxU0UZkq3wC9rSYxLLQyrReOva/F+t5sn2Ej4nXUYv5S5B/8OG1SaQExbNIHs8iACWgNJdD5ZDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:48:08.838512Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.11351","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e585a6f5ae22d93bcc8b673df635981dd323d35baf1622b10cce799e1eb7a6ab","sha256:d7ea1887fb2a4279cf366ff1acf52b20cf7e29d4b9b47f604b177872e354a901"],"state_sha256":"4630e8df43ac0546ffe983e4a5b7193102e00a7b6917031678c220bc6ce64dfa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YbfFJGGYo2hitIWoki5awzBpcFic2S9/DRY5s/zbqFgYqJZlG2Hg2QqWzfoEAcEY5iYNPSqiCvy/EZ4O/cNfAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T14:17:18.570686Z","bundle_sha256":"3596167b7fd20f5ad9a5330e14de0da3a50e139d8961607a88dae73ad6283e63"}}