{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XSN5VSRD6KK5FPOBAJ7ENG3ODE","short_pith_number":"pith:XSN5VSRD","canonical_record":{"source":{"id":"2402.13042","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-20T14:31:16Z","cross_cats_sorted":[],"title_canon_sha256":"ef6bdac3f29a30c4f5af20badc021d603b0065cb30a5b720e0bdd6daaed6a62b","abstract_canon_sha256":"2fa664654011843f7280500367e5f7c89db477dab86902c1dcddbd67f22eea09"},"schema_version":"1.0"},"canonical_sha256":"bc9bdaca23f295d2bdc1027e469b6e1903ba23b3f9f8c740bf4ffcc5f7a4f202","source":{"kind":"arxiv","id":"2402.13042","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.13042","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"arxiv_version","alias_value":"2402.13042v3","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13042","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_12","alias_value":"XSN5VSRD6KK5","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_16","alias_value":"XSN5VSRD6KK5FPOB","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_8","alias_value":"XSN5VSRD","created_at":"2026-07-05T11:04:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XSN5VSRD6KK5FPOBAJ7ENG3ODE","target":"record","payload":{"canonical_record":{"source":{"id":"2402.13042","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-20T14:31:16Z","cross_cats_sorted":[],"title_canon_sha256":"ef6bdac3f29a30c4f5af20badc021d603b0065cb30a5b720e0bdd6daaed6a62b","abstract_canon_sha256":"2fa664654011843f7280500367e5f7c89db477dab86902c1dcddbd67f22eea09"},"schema_version":"1.0"},"canonical_sha256":"bc9bdaca23f295d2bdc1027e469b6e1903ba23b3f9f8c740bf4ffcc5f7a4f202","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:35.806210Z","signature_b64":"V4l3sfVs/3LO6RhdtrdotBuLIQ8oFrCI+/kqSdQIzZwi46bOBvzfn0SfWq7nh40Z9X04rDDpu2B7CA7oHrjNDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc9bdaca23f295d2bdc1027e469b6e1903ba23b3f9f8c740bf4ffcc5f7a4f202","last_reissued_at":"2026-07-05T11:04:35.805695Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:35.805695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.13042","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:04:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f1iBdEf9hQOFoNTpswiX95SrVB1yr/EYktMdVzfWo/CXeaCJBnVaGAH87cGQ8jU6JkdJuMe/Z2N8l2e+S9VBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:30:58.065762Z"},"content_sha256":"1b2ddf185da639ab1d7f4a9831b2c8b39a729b1667f42a7ef43288c98b225d7d","schema_version":"1.0","event_id":"sha256:1b2ddf185da639ab1d7f4a9831b2c8b39a729b1667f42a7ef43288c98b225d7d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XSN5VSRD6KK5FPOBAJ7ENG3ODE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Not all distributional shifts are equal: Fine-grained robust conformal inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiahao Ai, Zhimei Ren","submitted_at":"2024-02-20T14:31:16Z","abstract_excerpt":"We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training samples to adjust for an identifiable covariate shift while protecting against worst-case conditional distribution shift bounded in an $f$-divergence ball. Based on ideas from conformal inference and distributionally robust learning, we present an algorithm that outputs (approximately"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13042","kind":"arxiv","version":3},"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/2402.13042/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":"2026-07-05T11:04:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s9bcb9AUOPYcmaOisepN05blrfgzblNRhaFG68JSrn/Ca556EchDznMpan+YnHe6ccYrG6/KmGpjlTh4dBZGCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:30:58.066332Z"},"content_sha256":"f47a3a07ee77936c97cf55d12dc364c0aa5cf86ee83b548e92ae3cb19b5ec579","schema_version":"1.0","event_id":"sha256:f47a3a07ee77936c97cf55d12dc364c0aa5cf86ee83b548e92ae3cb19b5ec579"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/bundle.json","state_url":"https://pith.science/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/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":"2026-08-14T08:30:58Z","links":{"resolver":"https://pith.science/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE","bundle":"https://pith.science/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/bundle.json","state":"https://pith.science/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XSN5VSRD6KK5FPOBAJ7ENG3ODE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XSN5VSRD6KK5FPOBAJ7ENG3ODE","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":"2fa664654011843f7280500367e5f7c89db477dab86902c1dcddbd67f22eea09","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-20T14:31:16Z","title_canon_sha256":"ef6bdac3f29a30c4f5af20badc021d603b0065cb30a5b720e0bdd6daaed6a62b"},"schema_version":"1.0","source":{"id":"2402.13042","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.13042","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"arxiv_version","alias_value":"2402.13042v3","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13042","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_12","alias_value":"XSN5VSRD6KK5","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_16","alias_value":"XSN5VSRD6KK5FPOB","created_at":"2026-07-05T11:04:35Z"},{"alias_kind":"pith_short_8","alias_value":"XSN5VSRD","created_at":"2026-07-05T11:04:35Z"}],"graph_snapshots":[{"event_id":"sha256:f47a3a07ee77936c97cf55d12dc364c0aa5cf86ee83b548e92ae3cb19b5ec579","target":"graph","created_at":"2026-07-05T11:04:35Z","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/2402.13042/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training samples to adjust for an identifiable covariate shift while protecting against worst-case conditional distribution shift bounded in an $f$-divergence ball. Based on ideas from conformal inference and distributionally robust learning, we present an algorithm that outputs (approximately","authors_text":"Jiahao Ai, Zhimei Ren","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-20T14:31:16Z","title":"Not all distributional shifts are equal: Fine-grained robust conformal inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13042","kind":"arxiv","version":3},"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":"sha256:1b2ddf185da639ab1d7f4a9831b2c8b39a729b1667f42a7ef43288c98b225d7d","target":"record","created_at":"2026-07-05T11:04:35Z","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":"2fa664654011843f7280500367e5f7c89db477dab86902c1dcddbd67f22eea09","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-02-20T14:31:16Z","title_canon_sha256":"ef6bdac3f29a30c4f5af20badc021d603b0065cb30a5b720e0bdd6daaed6a62b"},"schema_version":"1.0","source":{"id":"2402.13042","kind":"arxiv","version":3}},"canonical_sha256":"bc9bdaca23f295d2bdc1027e469b6e1903ba23b3f9f8c740bf4ffcc5f7a4f202","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc9bdaca23f295d2bdc1027e469b6e1903ba23b3f9f8c740bf4ffcc5f7a4f202","first_computed_at":"2026-07-05T11:04:35.805695Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:35.805695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V4l3sfVs/3LO6RhdtrdotBuLIQ8oFrCI+/kqSdQIzZwi46bOBvzfn0SfWq7nh40Z9X04rDDpu2B7CA7oHrjNDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:35.806210Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.13042","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b2ddf185da639ab1d7f4a9831b2c8b39a729b1667f42a7ef43288c98b225d7d","sha256:f47a3a07ee77936c97cf55d12dc364c0aa5cf86ee83b548e92ae3cb19b5ec579"],"state_sha256":"fd6464a15eb2668b01383b477f62a590cd6e7b027a68fb3965462f16dff35367"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bcRWJLezW46J8i5TwihVmSImALp4FjBc70xKPJJBKRr2Amu3iKN1Fta3oLTn61xcwrRQpV8iZ09ArYBTJ+CMBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T08:30:58.070150Z","bundle_sha256":"f660db371e798a00b98673ce203d284aa9a10449ef287e6fd296e7316ed7fc0d"}}