{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QGHQWM6FJEYMGBIDIZPWGFVKAO","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":"2e029a22c7d2c7eccb4cb853de667710e9b7f307b618addbe54ec5a285a13d76","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-03-29T01:01:02Z","title_canon_sha256":"56977c8389ca4962491b5561f86d0f7a2cdb935b5977a0fa47bc671f9520dfbe"},"schema_version":"1.0","source":{"id":"2503.22923","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.22923","created_at":"2026-07-05T11:27:53Z"},{"alias_kind":"arxiv_version","alias_value":"2503.22923v2","created_at":"2026-07-05T11:27:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22923","created_at":"2026-07-05T11:27:53Z"},{"alias_kind":"pith_short_12","alias_value":"QGHQWM6FJEYM","created_at":"2026-07-05T11:27:53Z"},{"alias_kind":"pith_short_16","alias_value":"QGHQWM6FJEYMGBID","created_at":"2026-07-05T11:27:53Z"},{"alias_kind":"pith_short_8","alias_value":"QGHQWM6F","created_at":"2026-07-05T11:27:53Z"}],"graph_snapshots":[{"event_id":"sha256:21bc24ddc7866f4b015b9a63a4c09390cf001e0d3f4a563730686acf6b5dd377","target":"graph","created_at":"2026-07-05T11:27:53Z","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/2503.22923/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Distributionally robust optimization (DRO) is a powerful technique to train robust models against data distribution shift. This paper aims to solve regularized nonconvex DRO problems, where the uncertainty set is modeled by a so-called generalized Sinkhorn distance and the loss function is nonconvex and possibly unbounded. Such a distance allows to model uncertainty of distributions with different probability supports and divergence functions. For this class of regularized DRO problems, we derive a novel dual formulation taking the form of nested stochastic optimization, where the dual variabl","authors_text":"Yi Zhou, Yufeng Yang, Zhaosong Lu","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-03-29T01:01:02Z","title":"Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22923","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":"sha256:88068661d072247d646938b00d95fdab2b0ff480eebcbb1e9dddda6215b1be1b","target":"record","created_at":"2026-07-05T11:27:53Z","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":"2e029a22c7d2c7eccb4cb853de667710e9b7f307b618addbe54ec5a285a13d76","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-03-29T01:01:02Z","title_canon_sha256":"56977c8389ca4962491b5561f86d0f7a2cdb935b5977a0fa47bc671f9520dfbe"},"schema_version":"1.0","source":{"id":"2503.22923","kind":"arxiv","version":2}},"canonical_sha256":"818f0b33c54930c30503465f6316aa03bcec9597128f48910c7f3f0efa37f0cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"818f0b33c54930c30503465f6316aa03bcec9597128f48910c7f3f0efa37f0cb","first_computed_at":"2026-07-05T11:27:53.533704Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:27:53.533704Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w0zwOB7kTGxLTkv33Hkf/s1vwjvmp9kVKZECDH6yQykTYmaFIXM0dnQkMkBInVmhFbEFjhvKEobSyQVy9PSlBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:27:53.534203Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.22923","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88068661d072247d646938b00d95fdab2b0ff480eebcbb1e9dddda6215b1be1b","sha256:21bc24ddc7866f4b015b9a63a4c09390cf001e0d3f4a563730686acf6b5dd377"],"state_sha256":"660d5cd6d8cf95587b43b8767b03ad85df804fa2c0de355ba448f52f10605746"}