{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VF2HG2IB3DP6UJEMBYZ4WFC57E","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":"f1a965837a6fa35d2e5e8dfef8acb14899f484edd0cac0a94ca54e60df4523ce","cross_cats_sorted":["cs.LG","cs.NA","math.DS","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-22T01:33:58Z","title_canon_sha256":"bd0ef0dbf1b93b4ddb4465c0161b3db2816012c402c3ddfe61291d7036ab7f6c"},"schema_version":"1.0","source":{"id":"2510.19161","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.19161","created_at":"2026-07-29T01:25:30Z"},{"alias_kind":"arxiv_version","alias_value":"2510.19161v2","created_at":"2026-07-29T01:25:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.19161","created_at":"2026-07-29T01:25:30Z"},{"alias_kind":"pith_short_12","alias_value":"VF2HG2IB3DP6","created_at":"2026-07-29T01:25:30Z"},{"alias_kind":"pith_short_16","alias_value":"VF2HG2IB3DP6UJEM","created_at":"2026-07-29T01:25:30Z"},{"alias_kind":"pith_short_8","alias_value":"VF2HG2IB","created_at":"2026-07-29T01:25:30Z"}],"graph_snapshots":[{"event_id":"sha256:27b008504038ec648a95b26a0eab5a48c1baf0425db040a4aa3cbf3e0ae7d0e3","target":"graph","created_at":"2026-07-29T01:25:30Z","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/2510.19161/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantifying and predicting rare and extreme events is challenging because such events are infrequent, severe, and expensive to simulate. Existing data-driven methods often require multiple extremes in the training data or sampling process, leading to accurate predictions in quiescent regimes but high epistemic uncertainty in extreme-event regions. To overcome this limitation, we introduce Extreme Event Aware ($\\eta$-) Learning, which does not require extreme events in the available data. The method reduces uncertainty even in uncharted extreme regimes by enforcing during training the statistic","authors_text":"Kai Chang, Themistoklis P. Sapsis","cross_cats":["cs.LG","cs.NA","math.DS","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-22T01:33:58Z","title":"Extreme Event Aware ($\\eta$-) Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.19161","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:ae5376a6768c765b621f482deb7559f5c87dbfc34e7e90cbf03f8139be77ef3a","target":"record","created_at":"2026-07-29T01:25:30Z","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":"f1a965837a6fa35d2e5e8dfef8acb14899f484edd0cac0a94ca54e60df4523ce","cross_cats_sorted":["cs.LG","cs.NA","math.DS","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-22T01:33:58Z","title_canon_sha256":"bd0ef0dbf1b93b4ddb4465c0161b3db2816012c402c3ddfe61291d7036ab7f6c"},"schema_version":"1.0","source":{"id":"2510.19161","kind":"arxiv","version":2}},"canonical_sha256":"a974736901d8dfea248c0e33cb145df93723b5535589fe4bb62a1cbb7f035f22","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a974736901d8dfea248c0e33cb145df93723b5535589fe4bb62a1cbb7f035f22","first_computed_at":"2026-07-29T01:25:30.686852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-29T01:25:30.686852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pcdq5HLbkyFhgZrD3DVl8dikTKku9oKbqWfofjlvg6R+/UZBd6CikY+SdoNzHyDzl/7HSF5cHCoktOiS34+ICw==","signature_status":"signed_v1","signed_at":"2026-07-29T01:25:30.687879Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.19161","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae5376a6768c765b621f482deb7559f5c87dbfc34e7e90cbf03f8139be77ef3a","sha256:27b008504038ec648a95b26a0eab5a48c1baf0425db040a4aa3cbf3e0ae7d0e3"],"state_sha256":"98bc29c33545a9f66472e40ff90c19eec3020cf480607464881ef987bdcdaa98"}