{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2OPS2V7433N742LOK34MYUXS7X","short_pith_number":"pith:2OPS2V74","schema_version":"1.0","canonical_sha256":"d39f2d57fcdedbfe696e56f8cc52f2fdfd03ebfbe0348ff5a641ae30ec16f7f5","source":{"kind":"arxiv","id":"1907.07307","version":2},"attestation_state":"computed","paper":{"title":"Dynamic optimization with side information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Bradley Sturt, Christopher McCord, Dimitris Bertsimas","submitted_at":"2019-07-17T02:38:37Z","abstract_excerpt":"We develop a tractable and flexible approach for incorporating side information into dynamic optimization under uncertainty. The proposed framework uses predictive machine learning methods (such as $k$-nearest neighbors, kernel regression, and random forests) to weight the relative importance of various data-driven uncertainty sets in a robust optimization formulation. Through a novel measure concentration result for a class of machine learning methods, we prove that the proposed approach is asymptotically optimal for multi-period stochastic programming with side information. We also describe "},"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":"1907.07307","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-07-17T02:38:37Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"958cd07e47524c4828843a8fa114fe48964108a6ca2f7be62e7c4321e79eb109","abstract_canon_sha256":"bf7345f6a0a5de6b7cbedf52d12f8dba063b31a8b5c9e6dd806a5ed539084062"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:21:16.096988Z","signature_b64":"RfDrS2YkTAi2tzyoOd87VoLvWx9NXvZdxJFy4wghPmIDJFmIqk4iW4rMRegnEDdOYXIVOfyIIvpPwaZKW7XzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d39f2d57fcdedbfe696e56f8cc52f2fdfd03ebfbe0348ff5a641ae30ec16f7f5","last_reissued_at":"2026-07-05T01:21:16.096409Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:21:16.096409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic optimization with side information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Bradley Sturt, Christopher McCord, Dimitris Bertsimas","submitted_at":"2019-07-17T02:38:37Z","abstract_excerpt":"We develop a tractable and flexible approach for incorporating side information into dynamic optimization under uncertainty. The proposed framework uses predictive machine learning methods (such as $k$-nearest neighbors, kernel regression, and random forests) to weight the relative importance of various data-driven uncertainty sets in a robust optimization formulation. Through a novel measure concentration result for a class of machine learning methods, we prove that the proposed approach is asymptotically optimal for multi-period stochastic programming with side information. We also describe "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07307","kind":"arxiv","version":2},"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/1907.07307/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":"1907.07307","created_at":"2026-07-05T01:21:16.096476+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.07307v2","created_at":"2026-07-05T01:21:16.096476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07307","created_at":"2026-07-05T01:21:16.096476+00:00"},{"alias_kind":"pith_short_12","alias_value":"2OPS2V7433N7","created_at":"2026-07-05T01:21:16.096476+00:00"},{"alias_kind":"pith_short_16","alias_value":"2OPS2V7433N742LO","created_at":"2026-07-05T01:21:16.096476+00:00"},{"alias_kind":"pith_short_8","alias_value":"2OPS2V74","created_at":"2026-07-05T01:21:16.096476+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/2OPS2V7433N742LOK34MYUXS7X","json":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X.json","graph_json":"https://pith.science/api/pith-number/2OPS2V7433N742LOK34MYUXS7X/graph.json","events_json":"https://pith.science/api/pith-number/2OPS2V7433N742LOK34MYUXS7X/events.json","paper":"https://pith.science/paper/2OPS2V74"},"agent_actions":{"view_html":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X","download_json":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X.json","view_paper":"https://pith.science/paper/2OPS2V74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.07307&json=true","fetch_graph":"https://pith.science/api/pith-number/2OPS2V7433N742LOK34MYUXS7X/graph.json","fetch_events":"https://pith.science/api/pith-number/2OPS2V7433N742LOK34MYUXS7X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X/action/storage_attestation","attest_author":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X/action/author_attestation","sign_citation":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X/action/citation_signature","submit_replication":"https://pith.science/pith/2OPS2V7433N742LOK34MYUXS7X/action/replication_record"}},"created_at":"2026-07-05T01:21:16.096476+00:00","updated_at":"2026-07-05T01:21:16.096476+00:00"}