{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VM4KAQYGYYKANAQ7JX4Z6KL2RQ","short_pith_number":"pith:VM4KAQYG","schema_version":"1.0","canonical_sha256":"ab38a04306c61406821f4df99f297a8c17b6bbd4a3f1de022e814fc3657e2ed4","source":{"kind":"arxiv","id":"2607.13550","version":1},"attestation_state":"computed","paper":{"title":"Parallel gradient boosting for flexible estimation of conditional distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bruno Falissard (CESP), CB, EVDG), Mohammed Sedki (CESP), Nicolas Vayatis (CB), R\\'emy Chapelle (CESP","submitted_at":"2026-07-15T07:56:09Z","abstract_excerpt":"Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years. Among them is the prediction of entire conditional distributions rather than single functionals, which can often be framed as a multi-output regression problem, for example multiple quantile regression. Addressing such problems with classical implementations of boosting is computationally challenging, because usually one base model is trained for each target at every iterati"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.13550","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-15T07:56:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c6836064d8497b4a8fa007b8922764f563ac794c3cb68548d0c9bfc5f87467d1","abstract_canon_sha256":"c5b07477a1c25e3a4746aa80095702245e469e3129b1d550535e8254cae768bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T01:22:53.694156Z","signature_b64":"w0Vxc0ZI9wp3IVPhM3Hrq+Iqc8ZLPHonW2GowK66OFCwmIfJPHB+rdUXE4O8UDtDo2M1XLQlkpZkAwXLtXRCCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab38a04306c61406821f4df99f297a8c17b6bbd4a3f1de022e814fc3657e2ed4","last_reissued_at":"2026-07-16T01:22:53.693276Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T01:22:53.693276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel gradient boosting for flexible estimation of conditional distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bruno Falissard (CESP), CB, EVDG), Mohammed Sedki (CESP), Nicolas Vayatis (CB), R\\'emy Chapelle (CESP","submitted_at":"2026-07-15T07:56:09Z","abstract_excerpt":"Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years. Among them is the prediction of entire conditional distributions rather than single functionals, which can often be framed as a multi-output regression problem, for example multiple quantile regression. Addressing such problems with classical implementations of boosting is computationally challenging, because usually one base model is trained for each target at every iterati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13550","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.13550/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.13550","created_at":"2026-07-16T01:22:53.693745+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13550v1","created_at":"2026-07-16T01:22:53.693745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13550","created_at":"2026-07-16T01:22:53.693745+00:00"},{"alias_kind":"pith_short_12","alias_value":"VM4KAQYGYYKA","created_at":"2026-07-16T01:22:53.693745+00:00"},{"alias_kind":"pith_short_16","alias_value":"VM4KAQYGYYKANAQ7","created_at":"2026-07-16T01:22:53.693745+00:00"},{"alias_kind":"pith_short_8","alias_value":"VM4KAQYG","created_at":"2026-07-16T01:22:53.693745+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ","json":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ.json","graph_json":"https://pith.science/api/pith-number/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/graph.json","events_json":"https://pith.science/api/pith-number/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/events.json","paper":"https://pith.science/paper/VM4KAQYG"},"agent_actions":{"view_html":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ","download_json":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ.json","view_paper":"https://pith.science/paper/VM4KAQYG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13550&json=true","fetch_graph":"https://pith.science/api/pith-number/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/graph.json","fetch_events":"https://pith.science/api/pith-number/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/action/storage_attestation","attest_author":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/action/author_attestation","sign_citation":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/action/citation_signature","submit_replication":"https://pith.science/pith/VM4KAQYGYYKANAQ7JX4Z6KL2RQ/action/replication_record"}},"created_at":"2026-07-16T01:22:53.693745+00:00","updated_at":"2026-07-16T01:22:53.693745+00:00"}