{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:M36VHTVQDNVPYCGIGEEDW6RI35","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":"779a4616bc2b9de91f673912769240b422b35ae18144a3d10ae60f68c5b1f6bd","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-11T23:28:49Z","title_canon_sha256":"d47d4ef3db9ae572f26c7181e85351de99d3794101b88e921ecde45e110590e5"},"schema_version":"1.0","source":{"id":"2109.05389","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.05389","created_at":"2026-07-05T03:13:38Z"},{"alias_kind":"arxiv_version","alias_value":"2109.05389v1","created_at":"2026-07-05T03:13:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.05389","created_at":"2026-07-05T03:13:38Z"},{"alias_kind":"pith_short_12","alias_value":"M36VHTVQDNVP","created_at":"2026-07-05T03:13:38Z"},{"alias_kind":"pith_short_16","alias_value":"M36VHTVQDNVPYCGI","created_at":"2026-07-05T03:13:38Z"},{"alias_kind":"pith_short_8","alias_value":"M36VHTVQ","created_at":"2026-07-05T03:13:38Z"}],"graph_snapshots":[{"event_id":"sha256:2e64f14e4febd4c7c05834872e43a05a8f535cb905098cda2c84a56c3a234709","target":"graph","created_at":"2026-07-05T03:13:38Z","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/2109.05389/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Loss minimization is a dominant paradigm in machine learning, where a predictor is trained to minimize some loss function that depends on an uncertain event (e.g., \"will it rain tomorrow?''). Different loss functions imply different learning algorithms and, at times, very different predictors. While widespread and appealing, a clear drawback of this approach is that the loss function may not be known at the time of learning, requiring the algorithm to use a best-guess loss function. We suggest a rigorous new paradigm for loss minimization in machine learning where the loss function can be igno","authors_text":"Adam Tauman Kalai, Omer Reingold, Parikshit Gopalan, Udi Wieder, Vatsal Sharan","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-11T23:28:49Z","title":"Omnipredictors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.05389","kind":"arxiv","version":1},"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":"sha256:2103abdc835d3a1fb9147c52c201c70b486b57d5816c5857f723fa4e7dcdaca3","target":"record","created_at":"2026-07-05T03:13:38Z","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":"779a4616bc2b9de91f673912769240b422b35ae18144a3d10ae60f68c5b1f6bd","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-11T23:28:49Z","title_canon_sha256":"d47d4ef3db9ae572f26c7181e85351de99d3794101b88e921ecde45e110590e5"},"schema_version":"1.0","source":{"id":"2109.05389","kind":"arxiv","version":1}},"canonical_sha256":"66fd53ceb01b6afc08c831083b7a28df590042f328d96e95f2af4e0a89ee6c3c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"66fd53ceb01b6afc08c831083b7a28df590042f328d96e95f2af4e0a89ee6c3c","first_computed_at":"2026-07-05T03:13:38.436558Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:13:38.436558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Vk2ya7j3T2OgXaSQTKc2Ud5qLXzALfRwrVqCgcezDqe9Wnvj0VekvrwypOG86fgL86IAfBrCC7VlpgW4p8jqBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:13:38.436973Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.05389","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2103abdc835d3a1fb9147c52c201c70b486b57d5816c5857f723fa4e7dcdaca3","sha256:2e64f14e4febd4c7c05834872e43a05a8f535cb905098cda2c84a56c3a234709"],"state_sha256":"560f36b0f092b80ac1c4fac2a48ff534ef212ddc42b3a2a9d38b1e64f0a67ef8"}