{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:KGPFD2KL4NPYMGHPHMA3Y2LKHM","short_pith_number":"pith:KGPFD2KL","canonical_record":{"source":{"id":"2007.04154","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2020-07-08T14:33:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d7e31fe6eb00cfa4b9dfd16f34a3190063ed4d77f215d0b56ddcd09e5f4dc9b4","abstract_canon_sha256":"fcdb6274366386259f94ea82a841f0b6002d90d366dc83a52c5291dd7b4b3670"},"schema_version":"1.0"},"canonical_sha256":"519e51e94be35f8618ef3b01bc696a3b1236090612bbb31e5b2be3d4aea74b95","source":{"kind":"arxiv","id":"2007.04154","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.04154","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"arxiv_version","alias_value":"2007.04154v1","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.04154","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_12","alias_value":"KGPFD2KL4NPY","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_16","alias_value":"KGPFD2KL4NPYMGHP","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_8","alias_value":"KGPFD2KL","created_at":"2026-07-05T01:17:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:KGPFD2KL4NPYMGHPHMA3Y2LKHM","target":"record","payload":{"canonical_record":{"source":{"id":"2007.04154","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2020-07-08T14:33:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d7e31fe6eb00cfa4b9dfd16f34a3190063ed4d77f215d0b56ddcd09e5f4dc9b4","abstract_canon_sha256":"fcdb6274366386259f94ea82a841f0b6002d90d366dc83a52c5291dd7b4b3670"},"schema_version":"1.0"},"canonical_sha256":"519e51e94be35f8618ef3b01bc696a3b1236090612bbb31e5b2be3d4aea74b95","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:17:21.555844Z","signature_b64":"6SGllhyIm1ognbsdGLlmsMV32nsz64EAWSQJxn5xQWI/ImQ2Aq0AuZ67MxCQ6QZTj1LK4kxol7K6xEA65A5jBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"519e51e94be35f8618ef3b01bc696a3b1236090612bbb31e5b2be3d4aea74b95","last_reissued_at":"2026-07-05T01:17:21.555345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:17:21.555345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.04154","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:17:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e8toEdrPRKGdwqXZ7M7WKNzft3obD9WfJh59W22giNF93aU343QLI/HiPXAq73RfOgwOs5mBoPA7FnP6yPVeCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:42.042219Z"},"content_sha256":"a5ca3dc7b83d3859de0990b726fb24ba4d055d927e28f377c3ce8d5a74d85459","schema_version":"1.0","event_id":"sha256:a5ca3dc7b83d3859de0990b726fb24ba4d055d927e28f377c3ce8d5a74d85459"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:KGPFD2KL4NPYMGHPHMA3Y2LKHM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust pricing and hedging via neural SDEs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-fin.MF","authors_text":"David \\v{S}i\\v{s}ka, Lukasz Szpruch, Marc Sabate-Vidales, Patryk Gierjatowicz, \\v{Z}an \\v{Z}uri\\v{c}","submitted_at":"2020-07-08T14:33:17Z","abstract_excerpt":"Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are \"black-boxes\" as individual parameters do not have meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.04154","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.04154/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:17:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZH0vivWtiJLLi217bKwCR78SZ4eav9QwL9sEukvEm3sGASgNlEvvRZ3BJGOSyxsc53J03ZXNOiaXanC5G0VGDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:59:42.042702Z"},"content_sha256":"5d11fc1fcedab3930374579a7e624d52942eeeac6ba89f07121d61b38929b051","schema_version":"1.0","event_id":"sha256:5d11fc1fcedab3930374579a7e624d52942eeeac6ba89f07121d61b38929b051"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/bundle.json","state_url":"https://pith.science/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T02:59:42Z","links":{"resolver":"https://pith.science/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM","bundle":"https://pith.science/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/bundle.json","state":"https://pith.science/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KGPFD2KL4NPYMGHPHMA3Y2LKHM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KGPFD2KL4NPYMGHPHMA3Y2LKHM","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":"fcdb6274366386259f94ea82a841f0b6002d90d366dc83a52c5291dd7b4b3670","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2020-07-08T14:33:17Z","title_canon_sha256":"d7e31fe6eb00cfa4b9dfd16f34a3190063ed4d77f215d0b56ddcd09e5f4dc9b4"},"schema_version":"1.0","source":{"id":"2007.04154","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.04154","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"arxiv_version","alias_value":"2007.04154v1","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.04154","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_12","alias_value":"KGPFD2KL4NPY","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_16","alias_value":"KGPFD2KL4NPYMGHP","created_at":"2026-07-05T01:17:21Z"},{"alias_kind":"pith_short_8","alias_value":"KGPFD2KL","created_at":"2026-07-05T01:17:21Z"}],"graph_snapshots":[{"event_id":"sha256:5d11fc1fcedab3930374579a7e624d52942eeeac6ba89f07121d61b38929b051","target":"graph","created_at":"2026-07-05T01:17:21Z","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/2007.04154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the door to more robust and data-driven model selection mechanisms. However, most machine learning models are \"black-boxes\" as individual parameters do not have meaningful interpretation. The aim of this paper is to combine the above approaches achieving the best of both worlds. Combining neural networks wit","authors_text":"David \\v{S}i\\v{s}ka, Lukasz Szpruch, Marc Sabate-Vidales, Patryk Gierjatowicz, \\v{Z}an \\v{Z}uri\\v{c}","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2020-07-08T14:33:17Z","title":"Robust pricing and hedging via neural SDEs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.04154","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:a5ca3dc7b83d3859de0990b726fb24ba4d055d927e28f377c3ce8d5a74d85459","target":"record","created_at":"2026-07-05T01:17:21Z","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":"fcdb6274366386259f94ea82a841f0b6002d90d366dc83a52c5291dd7b4b3670","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.MF","submitted_at":"2020-07-08T14:33:17Z","title_canon_sha256":"d7e31fe6eb00cfa4b9dfd16f34a3190063ed4d77f215d0b56ddcd09e5f4dc9b4"},"schema_version":"1.0","source":{"id":"2007.04154","kind":"arxiv","version":1}},"canonical_sha256":"519e51e94be35f8618ef3b01bc696a3b1236090612bbb31e5b2be3d4aea74b95","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"519e51e94be35f8618ef3b01bc696a3b1236090612bbb31e5b2be3d4aea74b95","first_computed_at":"2026-07-05T01:17:21.555345Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:17:21.555345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6SGllhyIm1ognbsdGLlmsMV32nsz64EAWSQJxn5xQWI/ImQ2Aq0AuZ67MxCQ6QZTj1LK4kxol7K6xEA65A5jBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:17:21.555844Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.04154","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5ca3dc7b83d3859de0990b726fb24ba4d055d927e28f377c3ce8d5a74d85459","sha256:5d11fc1fcedab3930374579a7e624d52942eeeac6ba89f07121d61b38929b051"],"state_sha256":"9ddb218d31c16ad4e256a356dee947cd620bb93dcf8d52e63d8e09cdb2673420"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ny2IaQy5f8gYWMP2YYnVmVXi4ivhm5yXjeAHMWNKjPnulysUd7WYLnNFcex3BzEe0sEKiKStix+rxSwmcv+LBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T02:59:42.045870Z","bundle_sha256":"efdb20ed3cd13b132e66f28d37256b9fd7e330b4e9ab83ab414da9b13cbcb683"}}