{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4P5TOJRNZYZ3JROXXLOTYTYNQC","short_pith_number":"pith:4P5TOJRN","schema_version":"1.0","canonical_sha256":"e3fb37262dce33b4c5d7badd3c4f0d8086a58b064517d74aff2a66b3459930e1","source":{"kind":"arxiv","id":"2507.05220","version":1},"attestation_state":"computed","paper":{"title":"QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amogh Inamdar, Benjamin Eyre, David Madras, Richard Zemel, Thomas P Zollo, Zhun Deng","submitted_at":"2025-07-07T17:33:18Z","abstract_excerpt":"As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorithms have emerged to combine a small amount of high-fidelity observed data with a much larger set of imputed model outputs to estimate some quantity of interest. Yet current hybrid-inference tools target only means or single quantiles, limiting their applicability for many critical domains and use cases. We present QuEst, a principled framework to merge observed and imputed data to deliver point estimates and rigorous"},"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":"2507.05220","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-07T17:33:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"797b501d6901077028357cbec9df9066a12dee441a019f99fcbb2d635947922e","abstract_canon_sha256":"078fda963a9c3ee4b84ed9300fc8bfdc9f3cbb8f6372f9c8d533d4da866af92b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:10.837261Z","signature_b64":"DIigzV4mV0kKpr3/u2SSxxjtCj9JXXBKg7D9w1k/SSQYRQ/X7A2He87OyMqvcgeeEO7vmijCnt/SJ4AT/mVxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3fb37262dce33b4c5d7badd3c4f0d8086a58b064517d74aff2a66b3459930e1","last_reissued_at":"2026-07-05T11:33:10.836803Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:10.836803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amogh Inamdar, Benjamin Eyre, David Madras, Richard Zemel, Thomas P Zollo, Zhun Deng","submitted_at":"2025-07-07T17:33:18Z","abstract_excerpt":"As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorithms have emerged to combine a small amount of high-fidelity observed data with a much larger set of imputed model outputs to estimate some quantity of interest. Yet current hybrid-inference tools target only means or single quantiles, limiting their applicability for many critical domains and use cases. We present QuEst, a principled framework to merge observed and imputed data to deliver point estimates and rigorous"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.05220","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/2507.05220/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":"2507.05220","created_at":"2026-07-05T11:33:10.836869+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.05220v1","created_at":"2026-07-05T11:33:10.836869+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.05220","created_at":"2026-07-05T11:33:10.836869+00:00"},{"alias_kind":"pith_short_12","alias_value":"4P5TOJRNZYZ3","created_at":"2026-07-05T11:33:10.836869+00:00"},{"alias_kind":"pith_short_16","alias_value":"4P5TOJRNZYZ3JROX","created_at":"2026-07-05T11:33:10.836869+00:00"},{"alias_kind":"pith_short_8","alias_value":"4P5TOJRN","created_at":"2026-07-05T11:33:10.836869+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00320","citing_title":"Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC","json":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC.json","graph_json":"https://pith.science/api/pith-number/4P5TOJRNZYZ3JROXXLOTYTYNQC/graph.json","events_json":"https://pith.science/api/pith-number/4P5TOJRNZYZ3JROXXLOTYTYNQC/events.json","paper":"https://pith.science/paper/4P5TOJRN"},"agent_actions":{"view_html":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC","download_json":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC.json","view_paper":"https://pith.science/paper/4P5TOJRN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.05220&json=true","fetch_graph":"https://pith.science/api/pith-number/4P5TOJRNZYZ3JROXXLOTYTYNQC/graph.json","fetch_events":"https://pith.science/api/pith-number/4P5TOJRNZYZ3JROXXLOTYTYNQC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC/action/storage_attestation","attest_author":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC/action/author_attestation","sign_citation":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC/action/citation_signature","submit_replication":"https://pith.science/pith/4P5TOJRNZYZ3JROXXLOTYTYNQC/action/replication_record"}},"created_at":"2026-07-05T11:33:10.836869+00:00","updated_at":"2026-07-05T11:33:10.836869+00:00"}