{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:HY3D7A3GGX7URKO2WEROZCLLQE","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":"a25e0097487725d74223756a137a12b98946813359573c77ec6eebf033c5defb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T20:19:25Z","title_canon_sha256":"4ba1bc232297f4ea7ca79ed32a83a77615b970f3ded9b218718f78d9cc67e8da"},"schema_version":"1.0","source":{"id":"2006.10138","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10138","created_at":"2026-07-05T03:31:12Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10138v5","created_at":"2026-07-05T03:31:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10138","created_at":"2026-07-05T03:31:12Z"},{"alias_kind":"pith_short_12","alias_value":"HY3D7A3GGX7U","created_at":"2026-07-05T03:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"HY3D7A3GGX7URKO2","created_at":"2026-07-05T03:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"HY3D7A3G","created_at":"2026-07-05T03:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:6ba672b6d0262308d419e7de6dcd91b754129ad61ad16a83cbabdc79dd0d8455","target":"graph","created_at":"2026-07-05T03:31:12Z","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/2006.10138/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications in machine learning for improving the robustness of neural networks. In the literature, most methods for solving DRO are based on stochastic primal-dual methods. However, primal-dual methods for DRO suffer from several drawbacks: (1) manipulating a high-dimensional dual variable corresponding to the size of data is time expensive; (2) they are not friendly to online learning where data is coming sequentially. To addre","authors_text":"Qi Qi, Rong Jin, Tianbao Yang, Yi Xu, Zhishuai Guo","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T20:19:25Z","title":"An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10138","kind":"arxiv","version":5},"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":"sha256:32202949fdec4f66fffb87f834fda09e535eb45a5337f5f77df723b8637ac907","target":"record","created_at":"2026-07-05T03:31:12Z","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":"a25e0097487725d74223756a137a12b98946813359573c77ec6eebf033c5defb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T20:19:25Z","title_canon_sha256":"4ba1bc232297f4ea7ca79ed32a83a77615b970f3ded9b218718f78d9cc67e8da"},"schema_version":"1.0","source":{"id":"2006.10138","kind":"arxiv","version":5}},"canonical_sha256":"3e363f836635ff48a9dab122ec896b81106d1d574f80bf5b85bc97ccb8375027","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3e363f836635ff48a9dab122ec896b81106d1d574f80bf5b85bc97ccb8375027","first_computed_at":"2026-07-05T03:31:12.072595Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:31:12.072595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A209ZRm/qEC6tec2JzEGbt7rsuyo6FhWS9t2DZGjOI3XYZe4SYr88nobMbnSALP+iUZPsw4Rwf2MWTLwn81zCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:31:12.072990Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.10138","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32202949fdec4f66fffb87f834fda09e535eb45a5337f5f77df723b8637ac907","sha256:6ba672b6d0262308d419e7de6dcd91b754129ad61ad16a83cbabdc79dd0d8455"],"state_sha256":"a22d98c35e41942bcd72db0b16f0194283798eaea2a7b13a5652ca0a041930f8"}