{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:Z4GCZLQJJ5TA2RFCR3KCKKOHI3","short_pith_number":"pith:Z4GCZLQJ","canonical_record":{"source":{"id":"1307.0846","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-07-02T20:51:40Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"750d74cb03e7e03fee8b1a821a06ecf6be34e79c44965f48db78f26912471b15","abstract_canon_sha256":"666c0ba058d3e9671d35d836e7ff834af7d5a0f19469324acacc4f9fd6150d34"},"schema_version":"1.0"},"canonical_sha256":"cf0c2cae094f660d44a28ed42529c746f9671d55fd73adef9905147505a620f7","source":{"kind":"arxiv","id":"1307.0846","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1307.0846","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"1307.0846v1","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1307.0846","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"Z4GCZLQJJ5TA","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_16","alias_value":"Z4GCZLQJJ5TA2RFC","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_8","alias_value":"Z4GCZLQJ","created_at":"2026-05-18T12:28:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:Z4GCZLQJJ5TA2RFCR3KCKKOHI3","target":"record","payload":{"canonical_record":{"source":{"id":"1307.0846","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-07-02T20:51:40Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"750d74cb03e7e03fee8b1a821a06ecf6be34e79c44965f48db78f26912471b15","abstract_canon_sha256":"666c0ba058d3e9671d35d836e7ff834af7d5a0f19469324acacc4f9fd6150d34"},"schema_version":"1.0"},"canonical_sha256":"cf0c2cae094f660d44a28ed42529c746f9671d55fd73adef9905147505a620f7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:19:23.904096Z","signature_b64":"5BtCccmHywZYOcTQh9ZwA3T2whqzpt9OPDQNx8yUOHNkLsfDMoV7nhzwMM0x1EPXX5Y1H18XZtZDIvKGHB/CDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf0c2cae094f660d44a28ed42529c746f9671d55fd73adef9905147505a620f7","last_reissued_at":"2026-05-18T03:19:23.903361Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:19:23.903361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1307.0846","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":"2026-05-18T03:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qp2D9FEwZfMGvtN3Kc0tvp7ruSZqS+DO+LgKeDQfdOC3HcCpfuvAAI7Tgi+EWeQpOlzBtw2SGP4H6Fm7ks2UAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:39:00.477624Z"},"content_sha256":"76586fd2f754994eba6ff64ea572c1af2d2e75446183a4e55a046586fd69411b","schema_version":"1.0","event_id":"sha256:76586fd2f754994eba6ff64ea572c1af2d2e75446183a4e55a046586fd69411b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:Z4GCZLQJJ5TA2RFCR3KCKKOHI3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-supervised Ranking Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Evgeni Tsivtsivadze, Tom Heskes","submitted_at":"2013-07-02T20:51:40Z","abstract_excerpt":"We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervised setting and allows efficient search for multiple, near-optimal solutions. Furthermore, we describe the extension of the algorithm suitable for combined ranking and regression tasks. In our experiments we demonstrate that the proposed algorithm outperforms several state-of-the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1307.0846","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":"2026-05-18T03:19:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9dfGjhaDwh82YEYvZhm3SBLERwwlwpjKKLS/PsJf39vS0qTKNQ081B0ZKTLP8h/MwqShdn0tePhfXiQnUqQvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T18:39:00.477967Z"},"content_sha256":"39f65253dc7197c9852687b82e870168f2af70296e1a8a95fb90ae146022067a","schema_version":"1.0","event_id":"sha256:39f65253dc7197c9852687b82e870168f2af70296e1a8a95fb90ae146022067a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/bundle.json","state_url":"https://pith.science/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/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":"2026-05-29T18:39:00Z","links":{"resolver":"https://pith.science/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3","bundle":"https://pith.science/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/bundle.json","state":"https://pith.science/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z4GCZLQJJ5TA2RFCR3KCKKOHI3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:Z4GCZLQJJ5TA2RFCR3KCKKOHI3","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":"666c0ba058d3e9671d35d836e7ff834af7d5a0f19469324acacc4f9fd6150d34","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-07-02T20:51:40Z","title_canon_sha256":"750d74cb03e7e03fee8b1a821a06ecf6be34e79c44965f48db78f26912471b15"},"schema_version":"1.0","source":{"id":"1307.0846","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1307.0846","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"arxiv_version","alias_value":"1307.0846v1","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1307.0846","created_at":"2026-05-18T03:19:23Z"},{"alias_kind":"pith_short_12","alias_value":"Z4GCZLQJJ5TA","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_16","alias_value":"Z4GCZLQJJ5TA2RFC","created_at":"2026-05-18T12:28:09Z"},{"alias_kind":"pith_short_8","alias_value":"Z4GCZLQJ","created_at":"2026-05-18T12:28:09Z"}],"graph_snapshots":[{"event_id":"sha256:39f65253dc7197c9852687b82e870168f2af70296e1a8a95fb90ae146022067a","target":"graph","created_at":"2026-05-18T03:19:23Z","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 propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervised setting and allows efficient search for multiple, near-optimal solutions. Furthermore, we describe the extension of the algorithm suitable for combined ranking and regression tasks. In our experiments we demonstrate that the proposed algorithm outperforms several state-of-the","authors_text":"Evgeni Tsivtsivadze, Tom Heskes","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-07-02T20:51:40Z","title":"Semi-supervised Ranking Pursuit"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1307.0846","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":"sha256:76586fd2f754994eba6ff64ea572c1af2d2e75446183a4e55a046586fd69411b","target":"record","created_at":"2026-05-18T03:19:23Z","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":"666c0ba058d3e9671d35d836e7ff834af7d5a0f19469324acacc4f9fd6150d34","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-07-02T20:51:40Z","title_canon_sha256":"750d74cb03e7e03fee8b1a821a06ecf6be34e79c44965f48db78f26912471b15"},"schema_version":"1.0","source":{"id":"1307.0846","kind":"arxiv","version":1}},"canonical_sha256":"cf0c2cae094f660d44a28ed42529c746f9671d55fd73adef9905147505a620f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf0c2cae094f660d44a28ed42529c746f9671d55fd73adef9905147505a620f7","first_computed_at":"2026-05-18T03:19:23.903361Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:19:23.903361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5BtCccmHywZYOcTQh9ZwA3T2whqzpt9OPDQNx8yUOHNkLsfDMoV7nhzwMM0x1EPXX5Y1H18XZtZDIvKGHB/CDw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:19:23.904096Z","signed_message":"canonical_sha256_bytes"},"source_id":"1307.0846","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76586fd2f754994eba6ff64ea572c1af2d2e75446183a4e55a046586fd69411b","sha256:39f65253dc7197c9852687b82e870168f2af70296e1a8a95fb90ae146022067a"],"state_sha256":"31963d2ce2a14b10ee37402630bc0d1127431c4f75a6486132dff55a96da49b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+CE7Z7DTNNdQJQfLFVDN5A/hfsCpUsySGqKdl1hIiFQip/xq+atyv0jTk0Ph+jK83XZU1UxY+mB7/ZHAbsTPBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T18:39:00.481294Z","bundle_sha256":"86aaa76a74a5f0f7a0ce9c334057575e5c945ddfae40615ac614fbba02b40124"}}