{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:GFEA427ZW7NIXOBJB6SBLEXHWV","short_pith_number":"pith:GFEA427Z","canonical_record":{"source":{"id":"2503.02173","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T01:30:28Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"6836efe9bf64861d92886c019f8e8b1bd9e93f1a9c386341ac5da9ff1876e0de","abstract_canon_sha256":"c66eed1f387960dc4a373cb4fc96fa8a7938230bca3bd930670e4cee43d20026"},"schema_version":"1.0"},"canonical_sha256":"31480e6bf9b7da8bb8290fa41592e7b57b2cbf6ffc143fad3cd293078945d863","source":{"kind":"arxiv","id":"2503.02173","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.02173","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"arxiv_version","alias_value":"2503.02173v1","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02173","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_12","alias_value":"GFEA427ZW7NI","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_16","alias_value":"GFEA427ZW7NIXOBJ","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_8","alias_value":"GFEA427Z","created_at":"2026-07-05T10:23:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:GFEA427ZW7NIXOBJB6SBLEXHWV","target":"record","payload":{"canonical_record":{"source":{"id":"2503.02173","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T01:30:28Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"6836efe9bf64861d92886c019f8e8b1bd9e93f1a9c386341ac5da9ff1876e0de","abstract_canon_sha256":"c66eed1f387960dc4a373cb4fc96fa8a7938230bca3bd930670e4cee43d20026"},"schema_version":"1.0"},"canonical_sha256":"31480e6bf9b7da8bb8290fa41592e7b57b2cbf6ffc143fad3cd293078945d863","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:38.621700Z","signature_b64":"+9ueCaxsk2yjx/dPyjEs6jlkQfPFaGixb6iXounqG6NxRJAkOQV6G4KALuA6QtLwuMGBbocTV64aZ8fAOENfAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31480e6bf9b7da8bb8290fa41592e7b57b2cbf6ffc143fad3cd293078945d863","last_reissued_at":"2026-07-05T10:23:38.620826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:38.620826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.02173","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:23:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eX9cr3dYDtIJANloat4Ggt1eHq56aEKOqrnTjNel8ieOF3qHsglaJw3G8gGlUGwoidM/kq0aSHUuThDXMPaiBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:53:37.046791Z"},"content_sha256":"fbd3d01986755f10df8605cd90417e414138433edcdee761f5716a03bebca08d","schema_version":"1.0","event_id":"sha256:fbd3d01986755f10df8605cd90417e414138433edcdee761f5716a03bebca08d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:GFEA427ZW7NIXOBJB6SBLEXHWV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Data to Uncertainty Sets: a Machine Learning Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Benjamin Boucher, Dimitris Bertsimas","submitted_at":"2025-03-04T01:30:28Z","abstract_excerpt":"Existing approaches of prescriptive analytics -- where inputs of an optimization model can be predicted by leveraging covariates in a machine learning model -- often attempt to optimize the mean value of an uncertain objective. However, when applied to uncertain constraints, these methods rarely work because satisfying a crucial constraint in expectation may result in a high probability of violation. To remedy this, we leverage robust optimization to protect a constraint against the uncertainty of a machine learning model's output. To do so, we design an uncertainty set based on the model's lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02173","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/2503.02173/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:23:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8FOmeslWVChzHG4nRFfNeNdzMGDsK1gefBxzK2fFgR0tIrfTiwgf9fL+Ia/WONXgj1OhX5Eb/VS7uDCvFhAYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:53:37.047406Z"},"content_sha256":"59e20b2ddc5080bccc92a4725ee9bc7c14bc36a30bc1abe4b3983fec6f8cb18e","schema_version":"1.0","event_id":"sha256:59e20b2ddc5080bccc92a4725ee9bc7c14bc36a30bc1abe4b3983fec6f8cb18e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/bundle.json","state_url":"https://pith.science/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T12:53:37Z","links":{"resolver":"https://pith.science/pith/GFEA427ZW7NIXOBJB6SBLEXHWV","bundle":"https://pith.science/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/bundle.json","state":"https://pith.science/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GFEA427ZW7NIXOBJB6SBLEXHWV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:GFEA427ZW7NIXOBJB6SBLEXHWV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c66eed1f387960dc4a373cb4fc96fa8a7938230bca3bd930670e4cee43d20026","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T01:30:28Z","title_canon_sha256":"6836efe9bf64861d92886c019f8e8b1bd9e93f1a9c386341ac5da9ff1876e0de"},"schema_version":"1.0","source":{"id":"2503.02173","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.02173","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"arxiv_version","alias_value":"2503.02173v1","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02173","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_12","alias_value":"GFEA427ZW7NI","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_16","alias_value":"GFEA427ZW7NIXOBJ","created_at":"2026-07-05T10:23:38Z"},{"alias_kind":"pith_short_8","alias_value":"GFEA427Z","created_at":"2026-07-05T10:23:38Z"}],"graph_snapshots":[{"event_id":"sha256:59e20b2ddc5080bccc92a4725ee9bc7c14bc36a30bc1abe4b3983fec6f8cb18e","target":"graph","created_at":"2026-07-05T10:23:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.02173/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing approaches of prescriptive analytics -- where inputs of an optimization model can be predicted by leveraging covariates in a machine learning model -- often attempt to optimize the mean value of an uncertain objective. However, when applied to uncertain constraints, these methods rarely work because satisfying a crucial constraint in expectation may result in a high probability of violation. To remedy this, we leverage robust optimization to protect a constraint against the uncertainty of a machine learning model's output. To do so, we design an uncertainty set based on the model's lo","authors_text":"Benjamin Boucher, Dimitris Bertsimas","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T01:30:28Z","title":"From Data to Uncertainty Sets: a Machine Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02173","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fbd3d01986755f10df8605cd90417e414138433edcdee761f5716a03bebca08d","target":"record","created_at":"2026-07-05T10:23:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c66eed1f387960dc4a373cb4fc96fa8a7938230bca3bd930670e4cee43d20026","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-04T01:30:28Z","title_canon_sha256":"6836efe9bf64861d92886c019f8e8b1bd9e93f1a9c386341ac5da9ff1876e0de"},"schema_version":"1.0","source":{"id":"2503.02173","kind":"arxiv","version":1}},"canonical_sha256":"31480e6bf9b7da8bb8290fa41592e7b57b2cbf6ffc143fad3cd293078945d863","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31480e6bf9b7da8bb8290fa41592e7b57b2cbf6ffc143fad3cd293078945d863","first_computed_at":"2026-07-05T10:23:38.620826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:23:38.620826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+9ueCaxsk2yjx/dPyjEs6jlkQfPFaGixb6iXounqG6NxRJAkOQV6G4KALuA6QtLwuMGBbocTV64aZ8fAOENfAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:23:38.621700Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.02173","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbd3d01986755f10df8605cd90417e414138433edcdee761f5716a03bebca08d","sha256:59e20b2ddc5080bccc92a4725ee9bc7c14bc36a30bc1abe4b3983fec6f8cb18e"],"state_sha256":"739f9f50e3f7df8e00db59ea9bb994fafeb348e9c99b161acf3455b9958dc561"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bkuXHrVcxdVegSrMqa087ByNkoppzlXuylnJJ8LblcK1TCFsiEVFyBWQDogNw9Bz+k21xUCm7XusiGHu/+tOBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T12:53:37.057712Z","bundle_sha256":"20edc0c924d10a2c8963ff73af0e064e9c96d31a9e00245ba27f3380750bc9d4"}}