{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:73U3TRUZQWUGDRVHDUPMXEI7T5","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":"7efb11dc904224014685c16078e36cfadce868338c19eacf9f30d64bba9a9748","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T04:16:27Z","title_canon_sha256":"b9c543d470fc032fcd409a7bb6507ad6c44145be15598f6773250ad1f937bc47"},"schema_version":"1.0","source":{"id":"2007.13982","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.13982","created_at":"2026-07-05T04:47:37Z"},{"alias_kind":"arxiv_version","alias_value":"2007.13982v2","created_at":"2026-07-05T04:47:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.13982","created_at":"2026-07-05T04:47:37Z"},{"alias_kind":"pith_short_12","alias_value":"73U3TRUZQWUG","created_at":"2026-07-05T04:47:37Z"},{"alias_kind":"pith_short_16","alias_value":"73U3TRUZQWUGDRVH","created_at":"2026-07-05T04:47:37Z"},{"alias_kind":"pith_short_8","alias_value":"73U3TRUZ","created_at":"2026-07-05T04:47:37Z"}],"graph_snapshots":[{"event_id":"sha256:339ed1505d7f34d403b148edebccf7171a9bc0c1a912f284ffa73adfa451bca5","target":"graph","created_at":"2026-07-05T04:47:37Z","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/2007.13982/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While modern large-scale datasets often consist of heterogeneous subpopulations -- for example, multiple demographic groups or multiple text corpora -- the standard practice of minimizing average loss fails to guarantee uniformly low losses across all subpopulations. We propose a convex procedure that controls the worst-case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically, we observe on lexical similarity, wine quality, and recidivism prediction tasks that our worst-ca","authors_text":"Hongseok Namkoong, John Duchi, Tatsunori Hashimoto","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T04:16:27Z","title":"Distributionally Robust Losses for Latent Covariate Mixtures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.13982","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":"sha256:2a3afb63be68ac81f789925942796533b54385d386d9b3c37638b538290deb8b","target":"record","created_at":"2026-07-05T04:47:37Z","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":"7efb11dc904224014685c16078e36cfadce868338c19eacf9f30d64bba9a9748","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-28T04:16:27Z","title_canon_sha256":"b9c543d470fc032fcd409a7bb6507ad6c44145be15598f6773250ad1f937bc47"},"schema_version":"1.0","source":{"id":"2007.13982","kind":"arxiv","version":2}},"canonical_sha256":"fee9b9c69985a861c6a71d1ecb911f9f65c8a72d7697bf0b3986c489103eec20","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fee9b9c69985a861c6a71d1ecb911f9f65c8a72d7697bf0b3986c489103eec20","first_computed_at":"2026-07-05T04:47:37.966387Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:47:37.966387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Oxh/GpL7471qjf/5v6rCmUSGjlbQAR0+fnRgD8RtGiYvmtu+OV1KmoB8aY3nA19ZpCipa/02Rlev62VzwKIVDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:47:37.966766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.13982","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a3afb63be68ac81f789925942796533b54385d386d9b3c37638b538290deb8b","sha256:339ed1505d7f34d403b148edebccf7171a9bc0c1a912f284ffa73adfa451bca5"],"state_sha256":"52d46d86effc0ab939ef4c26544d71c33dc7845f264189c2087411cbfd157b55"}