{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:2OPS2V7433N742LOK34MYUXS7X","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":"bf7345f6a0a5de6b7cbedf52d12f8dba063b31a8b5c9e6dd806a5ed539084062","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-07-17T02:38:37Z","title_canon_sha256":"958cd07e47524c4828843a8fa114fe48964108a6ca2f7be62e7c4321e79eb109"},"schema_version":"1.0","source":{"id":"1907.07307","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.07307","created_at":"2026-07-05T01:21:16Z"},{"alias_kind":"arxiv_version","alias_value":"1907.07307v2","created_at":"2026-07-05T01:21:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07307","created_at":"2026-07-05T01:21:16Z"},{"alias_kind":"pith_short_12","alias_value":"2OPS2V7433N7","created_at":"2026-07-05T01:21:16Z"},{"alias_kind":"pith_short_16","alias_value":"2OPS2V7433N742LO","created_at":"2026-07-05T01:21:16Z"},{"alias_kind":"pith_short_8","alias_value":"2OPS2V74","created_at":"2026-07-05T01:21:16Z"}],"graph_snapshots":[{"event_id":"sha256:6e94ab8e05861eefd8f0c527e8aa87c33388b1323326390c323ab0fe260a7088","target":"graph","created_at":"2026-07-05T01:21:16Z","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/1907.07307/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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 ","authors_text":"Bradley Sturt, Christopher McCord, Dimitris Bertsimas","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-07-17T02:38:37Z","title":"Dynamic optimization with side information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07307","kind":"arxiv","version":2},"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":"sha256:4c60c540912ad7ea46b9611f4d5b23b43d0483ae5d3e3450fea756bf51b3af7d","target":"record","created_at":"2026-07-05T01:21:16Z","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":"bf7345f6a0a5de6b7cbedf52d12f8dba063b31a8b5c9e6dd806a5ed539084062","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-07-17T02:38:37Z","title_canon_sha256":"958cd07e47524c4828843a8fa114fe48964108a6ca2f7be62e7c4321e79eb109"},"schema_version":"1.0","source":{"id":"1907.07307","kind":"arxiv","version":2}},"canonical_sha256":"d39f2d57fcdedbfe696e56f8cc52f2fdfd03ebfbe0348ff5a641ae30ec16f7f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d39f2d57fcdedbfe696e56f8cc52f2fdfd03ebfbe0348ff5a641ae30ec16f7f5","first_computed_at":"2026-07-05T01:21:16.096409Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:21:16.096409Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RfDrS2YkTAi2tzyoOd87VoLvWx9NXvZdxJFy4wghPmIDJFmIqk4iW4rMRegnEDdOYXIVOfyIIvpPwaZKW7XzCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:21:16.096988Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.07307","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c60c540912ad7ea46b9611f4d5b23b43d0483ae5d3e3450fea756bf51b3af7d","sha256:6e94ab8e05861eefd8f0c527e8aa87c33388b1323326390c323ab0fe260a7088"],"state_sha256":"3e6917e48c302951f6292e9f9481f4745c83fbeec9b54cbd4ff8ace1e157d8ff"}