{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KD3TMJLG3TANMIH3OEI4VXLOYS","short_pith_number":"pith:KD3TMJLG","schema_version":"1.0","canonical_sha256":"50f7362566dcc0d620fb7111cadd6ec498b72e7a487bdf5792555c52748808da","source":{"kind":"arxiv","id":"2505.23565","version":1},"attestation_state":"computed","paper":{"title":"DRO: A Python Library for Distributionally Robust Optimization in Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.MS","cs.NA","math.NA"],"primary_cat":"cs.LG","authors_text":"Henry Lam, Hongseok Namkoong, Jiashuo Liu, Jose Blanchet, Tianyu Wang","submitted_at":"2025-05-29T15:39:12Z","abstract_excerpt":"We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org."},"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":"2505.23565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-29T15:39:12Z","cross_cats_sorted":["cs.MS","cs.NA","math.NA"],"title_canon_sha256":"9b9a43c5721b025cfe54659cc87d56af5ce77699d9e75f36bc615d2477d1b2b6","abstract_canon_sha256":"9784bb7946caf88108f2cb0d42afd7a7adac83975cf7cdd7a431491cfdbebddd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:04.020181Z","signature_b64":"afrTDZRzUqWM9lBps5G+C+0yfI5dagho0vsoOsoklmQi8hditV2Y3tSHc0LPCWaMS7slT+S75Yg0m6ca9o27Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50f7362566dcc0d620fb7111cadd6ec498b72e7a487bdf5792555c52748808da","last_reissued_at":"2026-07-05T11:12:04.019694Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:04.019694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DRO: A Python Library for Distributionally Robust Optimization in Machine Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.MS","cs.NA","math.NA"],"primary_cat":"cs.LG","authors_text":"Henry Lam, Hongseok Namkoong, Jiashuo Liu, Jose Blanchet, Tianyu Wang","submitted_at":"2025-05-29T15:39:12Z","abstract_excerpt":"We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23565","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/2505.23565/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":"2505.23565","created_at":"2026-07-05T11:12:04.019751+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.23565v1","created_at":"2026-07-05T11:12:04.019751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23565","created_at":"2026-07-05T11:12:04.019751+00:00"},{"alias_kind":"pith_short_12","alias_value":"KD3TMJLG3TAN","created_at":"2026-07-05T11:12:04.019751+00:00"},{"alias_kind":"pith_short_16","alias_value":"KD3TMJLG3TANMIH3","created_at":"2026-07-05T11:12:04.019751+00:00"},{"alias_kind":"pith_short_8","alias_value":"KD3TMJLG","created_at":"2026-07-05T11:12:04.019751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04948","citing_title":"Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS","json":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS.json","graph_json":"https://pith.science/api/pith-number/KD3TMJLG3TANMIH3OEI4VXLOYS/graph.json","events_json":"https://pith.science/api/pith-number/KD3TMJLG3TANMIH3OEI4VXLOYS/events.json","paper":"https://pith.science/paper/KD3TMJLG"},"agent_actions":{"view_html":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS","download_json":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS.json","view_paper":"https://pith.science/paper/KD3TMJLG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.23565&json=true","fetch_graph":"https://pith.science/api/pith-number/KD3TMJLG3TANMIH3OEI4VXLOYS/graph.json","fetch_events":"https://pith.science/api/pith-number/KD3TMJLG3TANMIH3OEI4VXLOYS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS/action/storage_attestation","attest_author":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS/action/author_attestation","sign_citation":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS/action/citation_signature","submit_replication":"https://pith.science/pith/KD3TMJLG3TANMIH3OEI4VXLOYS/action/replication_record"}},"created_at":"2026-07-05T11:12:04.019751+00:00","updated_at":"2026-07-05T11:12:04.019751+00:00"}