{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:2DJ7EDCSFS5DGD4TSK6RRXCOIM","short_pith_number":"pith:2DJ7EDCS","canonical_record":{"source":{"id":"2103.12947","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-24T02:52:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c2366cbdd17f578e5f4cf4dc7733dedb55e0ae3a1d1c975d130a23735e282354","abstract_canon_sha256":"b417c8ae06358126249b5ea4ae687e0541c3e67dcdd7542f80cf52cdd4ddb84a"},"schema_version":"1.0"},"canonical_sha256":"d0d3f20c522cba330f9392bd18dc4e4320c503693cb5a0c706e4552aa5e62860","source":{"kind":"arxiv","id":"2103.12947","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.12947","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.12947v1","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.12947","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_12","alias_value":"2DJ7EDCSFS5D","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_16","alias_value":"2DJ7EDCSFS5DGD4T","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_8","alias_value":"2DJ7EDCS","created_at":"2026-07-05T02:26:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:2DJ7EDCSFS5DGD4TSK6RRXCOIM","target":"record","payload":{"canonical_record":{"source":{"id":"2103.12947","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-24T02:52:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c2366cbdd17f578e5f4cf4dc7733dedb55e0ae3a1d1c975d130a23735e282354","abstract_canon_sha256":"b417c8ae06358126249b5ea4ae687e0541c3e67dcdd7542f80cf52cdd4ddb84a"},"schema_version":"1.0"},"canonical_sha256":"d0d3f20c522cba330f9392bd18dc4e4320c503693cb5a0c706e4552aa5e62860","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:26:04.027719Z","signature_b64":"363o5hjBtufIqQekHliEVdKCWYJlGHlo995599EXqSJbr0SifxFqAzL3SO4ahefZJ4vFMKmDfdVuAEBmsevcAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0d3f20c522cba330f9392bd18dc4e4320c503693cb5a0c706e4552aa5e62860","last_reissued_at":"2026-07-05T02:26:04.027275Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:26:04.027275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.12947","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":"2026-07-05T02:26:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TsKl4vE9HEZEiPi6UL/fB9fZBCs5GgnWRhjeWDI+xu/xe1m4n6uxUQrk+NAIcvXJb5Y5bxanAro0tPbxB5VWAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:55:30.556366Z"},"content_sha256":"ffb3b7d22057f040593b16e8b7b0bf70ec9a07b0ed60b6947c6bd4b5270789b9","schema_version":"1.0","event_id":"sha256:ffb3b7d22057f040593b16e8b7b0bf70ec9a07b0ed60b6947c6bd4b5270789b9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:2DJ7EDCSFS5DGD4TSK6RRXCOIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learned Invariant Risk Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Inchul Choi, Jun-Hyun Bae, Minho Lee","submitted_at":"2021-03-24T02:52:48Z","abstract_excerpt":"Empirical Risk Minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on data obtained from out-of-distribution (OOD). To address this problem, Invariant Risk Minimization (IRM) objective was suggested to find invariant optimal predictor which is less affected by the changes in data distribution. However, even with such progress, IRMv1, the practical formulation of IRM, still shows performance degradation when there are not enough training data, and even fails to generalize to OOD, if the number of spurious correlations is larger than the number"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.12947","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/2103.12947/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":"2026-07-05T02:26:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U5jDT1LXSuKpIT9y27nigPW/PHilvF0xN1U3FDNFMhb72EBHIrf24ph7u/BXDRoeijnT6/203Uhz/oeSY9OpDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T09:55:30.556922Z"},"content_sha256":"956e9e21419d5b82669d741bf53fa8f1c0ae9f850e20a156e00a52919af74ab4","schema_version":"1.0","event_id":"sha256:956e9e21419d5b82669d741bf53fa8f1c0ae9f850e20a156e00a52919af74ab4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/bundle.json","state_url":"https://pith.science/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/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":"2026-08-09T09:55:30Z","links":{"resolver":"https://pith.science/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM","bundle":"https://pith.science/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/bundle.json","state":"https://pith.science/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2DJ7EDCSFS5DGD4TSK6RRXCOIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:2DJ7EDCSFS5DGD4TSK6RRXCOIM","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":"b417c8ae06358126249b5ea4ae687e0541c3e67dcdd7542f80cf52cdd4ddb84a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-24T02:52:48Z","title_canon_sha256":"c2366cbdd17f578e5f4cf4dc7733dedb55e0ae3a1d1c975d130a23735e282354"},"schema_version":"1.0","source":{"id":"2103.12947","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.12947","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"arxiv_version","alias_value":"2103.12947v1","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.12947","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_12","alias_value":"2DJ7EDCSFS5D","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_16","alias_value":"2DJ7EDCSFS5DGD4T","created_at":"2026-07-05T02:26:04Z"},{"alias_kind":"pith_short_8","alias_value":"2DJ7EDCS","created_at":"2026-07-05T02:26:04Z"}],"graph_snapshots":[{"event_id":"sha256:956e9e21419d5b82669d741bf53fa8f1c0ae9f850e20a156e00a52919af74ab4","target":"graph","created_at":"2026-07-05T02:26:04Z","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/2103.12947/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Empirical Risk Minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on data obtained from out-of-distribution (OOD). To address this problem, Invariant Risk Minimization (IRM) objective was suggested to find invariant optimal predictor which is less affected by the changes in data distribution. However, even with such progress, IRMv1, the practical formulation of IRM, still shows performance degradation when there are not enough training data, and even fails to generalize to OOD, if the number of spurious correlations is larger than the number","authors_text":"Inchul Choi, Jun-Hyun Bae, Minho Lee","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-24T02:52:48Z","title":"Meta-Learned Invariant Risk Minimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.12947","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:ffb3b7d22057f040593b16e8b7b0bf70ec9a07b0ed60b6947c6bd4b5270789b9","target":"record","created_at":"2026-07-05T02:26:04Z","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":"b417c8ae06358126249b5ea4ae687e0541c3e67dcdd7542f80cf52cdd4ddb84a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-24T02:52:48Z","title_canon_sha256":"c2366cbdd17f578e5f4cf4dc7733dedb55e0ae3a1d1c975d130a23735e282354"},"schema_version":"1.0","source":{"id":"2103.12947","kind":"arxiv","version":1}},"canonical_sha256":"d0d3f20c522cba330f9392bd18dc4e4320c503693cb5a0c706e4552aa5e62860","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0d3f20c522cba330f9392bd18dc4e4320c503693cb5a0c706e4552aa5e62860","first_computed_at":"2026-07-05T02:26:04.027275Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:26:04.027275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"363o5hjBtufIqQekHliEVdKCWYJlGHlo995599EXqSJbr0SifxFqAzL3SO4ahefZJ4vFMKmDfdVuAEBmsevcAg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:26:04.027719Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.12947","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ffb3b7d22057f040593b16e8b7b0bf70ec9a07b0ed60b6947c6bd4b5270789b9","sha256:956e9e21419d5b82669d741bf53fa8f1c0ae9f850e20a156e00a52919af74ab4"],"state_sha256":"4ba3578ee32c646a796471e045cd7062d8bf38e5f6afe7d65e860d265d0dab37"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eBBZVDWEq8DAhMlgYtb9LMYuL0a5unzmP8H6PBVCu/FDEOIUqJl9e+5jRfz4T5qV/3NhyFKrCtLI/Y1vpzVODw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T09:55:30.560277Z","bundle_sha256":"b290a7d0a0abfdc0979d5069e613c931da5a3b3f6f4a7701a075d43d3209686d"}}