{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LTYHFQ5C2O5FQAETRGRPTRFIEI","short_pith_number":"pith:LTYHFQ5C","canonical_record":{"source":{"id":"1904.03943","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T10:48:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6ddda68f88bfa8e08ff9d270d8a8e94bb6315f1828a9ed3e4a4c401f71bfbc7b","abstract_canon_sha256":"16ea298098b72c74b4fbffe682d4b47ad158f9e9040544c33a4c8ae8f97323ec"},"schema_version":"1.0"},"canonical_sha256":"5cf072c3a2d3ba58009389a2f9c4a8222cd14f425834ed19f0dd73011d682c2f","source":{"kind":"arxiv","id":"1904.03943","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.03943","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1904.03943v1","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.03943","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"LTYHFQ5C2O5F","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LTYHFQ5C2O5FQAET","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LTYHFQ5C","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LTYHFQ5C2O5FQAETRGRPTRFIEI","target":"record","payload":{"canonical_record":{"source":{"id":"1904.03943","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T10:48:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6ddda68f88bfa8e08ff9d270d8a8e94bb6315f1828a9ed3e4a4c401f71bfbc7b","abstract_canon_sha256":"16ea298098b72c74b4fbffe682d4b47ad158f9e9040544c33a4c8ae8f97323ec"},"schema_version":"1.0"},"canonical_sha256":"5cf072c3a2d3ba58009389a2f9c4a8222cd14f425834ed19f0dd73011d682c2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:07.611271Z","signature_b64":"GXVQI4s4+SNM8KuKdMi3MHKWTSJEiDf9XkEkSfkW0jtq9QIYNmwpOY+dHrhz9AvEbI1p3dQ+q2cRD997kSSeCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cf072c3a2d3ba58009389a2f9c4a8222cd14f425834ed19f0dd73011d682c2f","last_reissued_at":"2026-05-17T23:49:07.610711Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:07.610711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.03943","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":"2026-05-17T23:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E0uRA4pzoRaL5Bek+U03FJAcbYuFQTYf+CvQOSM+PriP+LwZkr/ugoZ8JeJhaWKw3ow7Hrp1K6Op9tLeSSanCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T02:16:41.637642Z"},"content_sha256":"29dece0aa5622b8f7d5bb4ceda6a8b7b556a412392f6f22ed7f73e33184a16f2","schema_version":"1.0","event_id":"sha256:29dece0aa5622b8f7d5bb4ceda6a8b7b556a412392f6f22ed7f73e33184a16f2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LTYHFQ5C2O5FQAETRGRPTRFIEI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Component-Wise Boosting of Targets for Multi-Output Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernd Bischl, Clemens Stachl, Daniel Schalk, Giuseppe Casalicchio, Quay Au, Ramona Schoedel","submitted_at":"2019-04-08T10:48:56Z","abstract_excerpt":"Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algorithm that uses the problem transformation method for multi-output prediction, while simultaneously learning the dependencies between target variables in a sparse and interpretable manner. In a first step, predictions are obtained for each target indi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.03943","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":"2026-05-17T23:49:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A3X+TEjA4mzwRP01DAZN7Cn1K6cgfqmB4h1naEYvhouQxJE6s9+HAxpNt7S4k+u5zhA5tXyrYrEdwX8aks5OBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-26T02:16:41.637981Z"},"content_sha256":"4e23e15150c79553a758b9190ac3ba2f930fb9c9dba4edac71f51082ce6cd422","schema_version":"1.0","event_id":"sha256:4e23e15150c79553a758b9190ac3ba2f930fb9c9dba4edac71f51082ce6cd422"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/bundle.json","state_url":"https://pith.science/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/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":"2026-07-26T02:16:41Z","links":{"resolver":"https://pith.science/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI","bundle":"https://pith.science/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/bundle.json","state":"https://pith.science/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LTYHFQ5C2O5FQAETRGRPTRFIEI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LTYHFQ5C2O5FQAETRGRPTRFIEI","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":"16ea298098b72c74b4fbffe682d4b47ad158f9e9040544c33a4c8ae8f97323ec","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T10:48:56Z","title_canon_sha256":"6ddda68f88bfa8e08ff9d270d8a8e94bb6315f1828a9ed3e4a4c401f71bfbc7b"},"schema_version":"1.0","source":{"id":"1904.03943","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.03943","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"arxiv_version","alias_value":"1904.03943v1","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.03943","created_at":"2026-05-17T23:49:07Z"},{"alias_kind":"pith_short_12","alias_value":"LTYHFQ5C2O5F","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LTYHFQ5C2O5FQAET","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LTYHFQ5C","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:4e23e15150c79553a758b9190ac3ba2f930fb9c9dba4edac71f51082ce6cd422","target":"graph","created_at":"2026-05-17T23:49:07Z","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":"Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algorithm that uses the problem transformation method for multi-output prediction, while simultaneously learning the dependencies between target variables in a sparse and interpretable manner. In a first step, predictions are obtained for each target indi","authors_text":"Bernd Bischl, Clemens Stachl, Daniel Schalk, Giuseppe Casalicchio, Quay Au, Ramona Schoedel","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T10:48:56Z","title":"Component-Wise Boosting of Targets for Multi-Output Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.03943","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":"sha256:29dece0aa5622b8f7d5bb4ceda6a8b7b556a412392f6f22ed7f73e33184a16f2","target":"record","created_at":"2026-05-17T23:49:07Z","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":"16ea298098b72c74b4fbffe682d4b47ad158f9e9040544c33a4c8ae8f97323ec","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T10:48:56Z","title_canon_sha256":"6ddda68f88bfa8e08ff9d270d8a8e94bb6315f1828a9ed3e4a4c401f71bfbc7b"},"schema_version":"1.0","source":{"id":"1904.03943","kind":"arxiv","version":1}},"canonical_sha256":"5cf072c3a2d3ba58009389a2f9c4a8222cd14f425834ed19f0dd73011d682c2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5cf072c3a2d3ba58009389a2f9c4a8222cd14f425834ed19f0dd73011d682c2f","first_computed_at":"2026-05-17T23:49:07.610711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:07.610711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GXVQI4s4+SNM8KuKdMi3MHKWTSJEiDf9XkEkSfkW0jtq9QIYNmwpOY+dHrhz9AvEbI1p3dQ+q2cRD997kSSeCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:07.611271Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.03943","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29dece0aa5622b8f7d5bb4ceda6a8b7b556a412392f6f22ed7f73e33184a16f2","sha256:4e23e15150c79553a758b9190ac3ba2f930fb9c9dba4edac71f51082ce6cd422"],"state_sha256":"e4308ab56dc097d5f17dab784eb943d160a35d06218330f56d464817829519be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jL6GYvKsPqPggUyTKQFOD3eZQxO19If/SyRJH+GrklpfKZ4oEyI8QzkqjWHLsmQsPOUX/SIsjkwBPtY/dyikCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-26T02:16:41.640057Z","bundle_sha256":"c8c46ed1081ed604ef4fb64f6a362bc6e9c71ad09f5b91571915243119129404"}}