{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QWT4LEBYKYB2NHEDSYAG46URIH","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":"916e17dec686649ad8ba61fb56c371ff591830911f0a7fe85aea3c174d249853","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-20T03:50:29Z","title_canon_sha256":"a4fbdfa831d2a6b2debe10dee3b00e7e893c66ccd2f3ab121fc28c00c5ca86c3"},"schema_version":"1.0","source":{"id":"1810.08750","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.08750","created_at":"2026-07-05T01:20:09Z"},{"alias_kind":"arxiv_version","alias_value":"1810.08750v6","created_at":"2026-07-05T01:20:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08750","created_at":"2026-07-05T01:20:09Z"},{"alias_kind":"pith_short_12","alias_value":"QWT4LEBYKYB2","created_at":"2026-07-05T01:20:09Z"},{"alias_kind":"pith_short_16","alias_value":"QWT4LEBYKYB2NHED","created_at":"2026-07-05T01:20:09Z"},{"alias_kind":"pith_short_8","alias_value":"QWT4LEBY","created_at":"2026-07-05T01:20:09Z"}],"graph_snapshots":[{"event_id":"sha256:8233094a41ddf3ee7a7789634165212e3fbf84cfb8c21454f296522e140295b8","target":"graph","created_at":"2026-07-05T01:20:09Z","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/1810.08750/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects. We develop and analyze a distributionally robust stochastic optimization (DRO) framework that learns a model providing good performance against perturbations to the data-generating distribution. We give a convex formulation for the problem, providing several convergence guarantees. We prove finite-sample minimax upper and lower bounds, showing that distributional robustness ","authors_text":"Hongseok Namkoong, John Duchi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08750","kind":"arxiv","version":6},"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":"sha256:98f68d4a46b8d6d0718d39566b94ceb33258d370a383bef8541917a0c61da61c","target":"record","created_at":"2026-07-05T01:20:09Z","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":"916e17dec686649ad8ba61fb56c371ff591830911f0a7fe85aea3c174d249853","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-20T03:50:29Z","title_canon_sha256":"a4fbdfa831d2a6b2debe10dee3b00e7e893c66ccd2f3ab121fc28c00c5ca86c3"},"schema_version":"1.0","source":{"id":"1810.08750","kind":"arxiv","version":6}},"canonical_sha256":"85a7c590385603a69c8396006e7a9141f2c664ac398ba53090a7f400dc419e99","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85a7c590385603a69c8396006e7a9141f2c664ac398ba53090a7f400dc419e99","first_computed_at":"2026-07-05T01:20:09.557311Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:20:09.557311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+rtCPEuQmlEUL+goZ+07tm3ol7JTp++rjVF3an+FlLpIw/Ms7oV7WrRxRvrCXJZ+a+nOD0gEqwoVzr+WPHojAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:20:09.557795Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.08750","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98f68d4a46b8d6d0718d39566b94ceb33258d370a383bef8541917a0c61da61c","sha256:8233094a41ddf3ee7a7789634165212e3fbf84cfb8c21454f296522e140295b8"],"state_sha256":"132ae72011cd57fc9eeafc1cdea3d16e8aef5f20f5ab6278d362b1ca878af82c"}