{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZVQS5J47GGJQ4IJME2BCABKZDL","short_pith_number":"pith:ZVQS5J47","schema_version":"1.0","canonical_sha256":"cd612ea79f31930e212c26822005591ac845b7051f013cf4f3b513f1ca72869e","source":{"kind":"arxiv","id":"2606.25188","version":1},"attestation_state":"computed","paper":{"title":"Efficient Analytic Uncertainty Quantification for Multi-Modal Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arnab Bhadury, James Harrison, Jasper Snoek, Jiawei Li, Jiayi Liu, Kun Jin, Liang Liu, Randolph Linderman, Sihan Liu, Sourabh Prakash Bansod, Yuening Li","submitted_at":"2026-06-23T21:31:42Z","abstract_excerpt":"Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the conditional label marginal satisfies single-peak parametric models, e.g., Gaussians, where the negative log-likelihood function simplifies to the mean square error. However, such single-peak assumptions fail in regression tasks featuring multi-modal distributions. On the other hand, semi-parametric methods which achieve strong regression performance for multi-modal distributions often lack efficient quantification on "},"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":"2606.25188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T21:31:42Z","cross_cats_sorted":[],"title_canon_sha256":"b71552e06559fce126d9b38b90389608864a545453f35948cd7a5e8fdd143793","abstract_canon_sha256":"91c05ccec177695d56d1c3fc4882be08eceaad94c79a55a2f2a38408c0eda950"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T00:18:20.373417Z","signature_b64":"VJtrSI1FhkzZKeBtOZ7mlzr9VrNijZu0ED8ArFgy84+B8dIMwTE60P6zq66QXR/qJ4zLhvGWgbj9HlKbNFcZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd612ea79f31930e212c26822005591ac845b7051f013cf4f3b513f1ca72869e","last_reissued_at":"2026-06-25T00:18:20.373004Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T00:18:20.373004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Analytic Uncertainty Quantification for Multi-Modal Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arnab Bhadury, James Harrison, Jasper Snoek, Jiawei Li, Jiayi Liu, Kun Jin, Liang Liu, Randolph Linderman, Sihan Liu, Sourabh Prakash Bansod, Yuening Li","submitted_at":"2026-06-23T21:31:42Z","abstract_excerpt":"Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the conditional label marginal satisfies single-peak parametric models, e.g., Gaussians, where the negative log-likelihood function simplifies to the mean square error. However, such single-peak assumptions fail in regression tasks featuring multi-modal distributions. On the other hand, semi-parametric methods which achieve strong regression performance for multi-modal distributions often lack efficient quantification on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25188","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/2606.25188/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":"2606.25188","created_at":"2026-06-25T00:18:20.373067+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.25188v1","created_at":"2026-06-25T00:18:20.373067+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25188","created_at":"2026-06-25T00:18:20.373067+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVQS5J47GGJQ","created_at":"2026-06-25T00:18:20.373067+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVQS5J47GGJQ4IJM","created_at":"2026-06-25T00:18:20.373067+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVQS5J47","created_at":"2026-06-25T00:18:20.373067+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/ZVQS5J47GGJQ4IJME2BCABKZDL","json":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL.json","graph_json":"https://pith.science/api/pith-number/ZVQS5J47GGJQ4IJME2BCABKZDL/graph.json","events_json":"https://pith.science/api/pith-number/ZVQS5J47GGJQ4IJME2BCABKZDL/events.json","paper":"https://pith.science/paper/ZVQS5J47"},"agent_actions":{"view_html":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL","download_json":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL.json","view_paper":"https://pith.science/paper/ZVQS5J47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.25188&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVQS5J47GGJQ4IJME2BCABKZDL/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVQS5J47GGJQ4IJME2BCABKZDL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL/action/storage_attestation","attest_author":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL/action/author_attestation","sign_citation":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL/action/citation_signature","submit_replication":"https://pith.science/pith/ZVQS5J47GGJQ4IJME2BCABKZDL/action/replication_record"}},"created_at":"2026-06-25T00:18:20.373067+00:00","updated_at":"2026-06-25T00:18:20.373067+00:00"}