{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PQPRYB4JPDR6LAUPFGAPBVLXHW","short_pith_number":"pith:PQPRYB4J","schema_version":"1.0","canonical_sha256":"7c1f1c078978e3e5828f2980f0d5773d96381216050decc0b10338c848f86b0d","source":{"kind":"arxiv","id":"2311.16487","version":4},"attestation_state":"computed","paper":{"title":"On the Robustness of Decision-Focused Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Yehya Farhat","submitted_at":"2023-11-28T04:34:04Z","abstract_excerpt":"Decision-Focused Learning (DFL) is an emerging learning paradigm that tackles the task of training a machine learning (ML) model to predict missing parameters of an incomplete optimization problem, where the missing parameters are predicted. DFL trains an ML model in an end-to-end system, by integrating the prediction and optimization tasks, providing better alignment of the training and testing objectives. DFL has shown a lot of promise and holds the capacity to revolutionize decision-making in many real-world applications. However, very little is known about the performance of these models u"},"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":"2311.16487","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-28T04:34:04Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"29f02226c5dc83a05f932f72d6edb23bdd5a8af4e51f401d0a4882b488a1abad","abstract_canon_sha256":"41549c8c8161ac00d3cc120a7a9d758615ebcb6e5e945e9e17d56e193f1d8340"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:01.786682Z","signature_b64":"z26WobiFFUblbY+i1SKIFvGL7agpbYAvjTAhf7ZCWRZ8CIdr/Js7OsVSZ1FsuMJ4xp7JaGZZvpOuGzKU8caoBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c1f1c078978e3e5828f2980f0d5773d96381216050decc0b10338c848f86b0d","last_reissued_at":"2026-07-05T11:24:01.786179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:01.786179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Robustness of Decision-Focused Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Yehya Farhat","submitted_at":"2023-11-28T04:34:04Z","abstract_excerpt":"Decision-Focused Learning (DFL) is an emerging learning paradigm that tackles the task of training a machine learning (ML) model to predict missing parameters of an incomplete optimization problem, where the missing parameters are predicted. DFL trains an ML model in an end-to-end system, by integrating the prediction and optimization tasks, providing better alignment of the training and testing objectives. DFL has shown a lot of promise and holds the capacity to revolutionize decision-making in many real-world applications. However, very little is known about the performance of these models u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16487","kind":"arxiv","version":4},"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/2311.16487/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":"2311.16487","created_at":"2026-07-05T11:24:01.786237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.16487v4","created_at":"2026-07-05T11:24:01.786237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16487","created_at":"2026-07-05T11:24:01.786237+00:00"},{"alias_kind":"pith_short_12","alias_value":"PQPRYB4JPDR6","created_at":"2026-07-05T11:24:01.786237+00:00"},{"alias_kind":"pith_short_16","alias_value":"PQPRYB4JPDR6LAUP","created_at":"2026-07-05T11:24:01.786237+00:00"},{"alias_kind":"pith_short_8","alias_value":"PQPRYB4J","created_at":"2026-07-05T11:24:01.786237+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/PQPRYB4JPDR6LAUPFGAPBVLXHW","json":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW.json","graph_json":"https://pith.science/api/pith-number/PQPRYB4JPDR6LAUPFGAPBVLXHW/graph.json","events_json":"https://pith.science/api/pith-number/PQPRYB4JPDR6LAUPFGAPBVLXHW/events.json","paper":"https://pith.science/paper/PQPRYB4J"},"agent_actions":{"view_html":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW","download_json":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW.json","view_paper":"https://pith.science/paper/PQPRYB4J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.16487&json=true","fetch_graph":"https://pith.science/api/pith-number/PQPRYB4JPDR6LAUPFGAPBVLXHW/graph.json","fetch_events":"https://pith.science/api/pith-number/PQPRYB4JPDR6LAUPFGAPBVLXHW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW/action/storage_attestation","attest_author":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW/action/author_attestation","sign_citation":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW/action/citation_signature","submit_replication":"https://pith.science/pith/PQPRYB4JPDR6LAUPFGAPBVLXHW/action/replication_record"}},"created_at":"2026-07-05T11:24:01.786237+00:00","updated_at":"2026-07-05T11:24:01.786237+00:00"}