{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:PGUCWBMEL4ZTY4X6E3SIZN36EC","short_pith_number":"pith:PGUCWBME","canonical_record":{"source":{"id":"2211.14578","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-11-26T14:40:19Z","cross_cats_sorted":["cs.LG","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"6526843b8594fb5b5e3db15a0552da9fea47b2f8d73d14f630e62ab73deaac05","abstract_canon_sha256":"283fe58fddfb63d766c8548e710ccc6f951d9f4b05b7dd26164c900b34ce17b5"},"schema_version":"1.0"},"canonical_sha256":"79a82b05845f333c72fe26e48cb77e209aa9ecf4b96433bfce2abbbcee1a4465","source":{"kind":"arxiv","id":"2211.14578","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.14578","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"arxiv_version","alias_value":"2211.14578v3","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14578","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_12","alias_value":"PGUCWBMEL4ZT","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_16","alias_value":"PGUCWBMEL4ZTY4X6","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_8","alias_value":"PGUCWBME","created_at":"2026-07-05T07:08:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:PGUCWBMEL4ZTY4X6E3SIZN36EC","target":"record","payload":{"canonical_record":{"source":{"id":"2211.14578","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-11-26T14:40:19Z","cross_cats_sorted":["cs.LG","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"6526843b8594fb5b5e3db15a0552da9fea47b2f8d73d14f630e62ab73deaac05","abstract_canon_sha256":"283fe58fddfb63d766c8548e710ccc6f951d9f4b05b7dd26164c900b34ce17b5"},"schema_version":"1.0"},"canonical_sha256":"79a82b05845f333c72fe26e48cb77e209aa9ecf4b96433bfce2abbbcee1a4465","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:53.912158Z","signature_b64":"aMiENaHBCPBKkbqhsd5Dfh75Z2+SHZFZFG/ZgC6iF14j7uKdAr8ykRHwD0C3heafe2MPibVVGvDFtlTaZiiKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79a82b05845f333c72fe26e48cb77e209aa9ecf4b96433bfce2abbbcee1a4465","last_reissued_at":"2026-07-05T07:08:53.911706Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:53.911706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.14578","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":"2026-07-05T07:08:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"voXIx5LOntiTrn/OflCuAdRpbwlNjVoro1LBt94CS8BH7J5SVMjXIjlY3ccu1byxV4COrVyAVi/qdXAJVG7bDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T03:26:49.273629Z"},"content_sha256":"4afa3f27422af615f54c09bb67e5ddd53ff717f4f4dd576bc4fa76f465be2604","schema_version":"1.0","event_id":"sha256:4afa3f27422af615f54c09bb67e5ddd53ff717f4f4dd576bc4fa76f465be2604"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:PGUCWBMEL4ZTY4X6E3SIZN36EC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Estimation and inference for transfer learning with high-dimensional quantile regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ME","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jiayu Huang, Mingqiu Wang, Yuanshan Wu","submitted_at":"2022-11-26T14:40:19Z","abstract_excerpt":"Transfer learning has become an essential technique to exploit information from the source domain to boost performance of the target task. Despite the prevalence in high-dimensional data, heterogeneity and heavy tails are insufficiently accounted for by current transfer learning approaches and thus may undermine the resulting performance. We propose a transfer learning procedure in the framework of high-dimensional quantile regression models to accommodate heterogeneity and heavy tails in the source and target domains. We establish error bounds of transfer learning estimator based on delicatel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14578","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/2211.14578/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":"2026-07-05T07:08:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vG/JUlTytuldd8zfWluDoMx7PGoi9rkt18l0nuA4FLL8xuxmI9ZuA3Dc2y+5xcnFb/+QmN29K6jYuk8fPbegCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T03:26:49.274573Z"},"content_sha256":"fb42f7a362a6e803e37d251fbc8be2b2989935b3561494bd279d19b52576e779","schema_version":"1.0","event_id":"sha256:fb42f7a362a6e803e37d251fbc8be2b2989935b3561494bd279d19b52576e779"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/bundle.json","state_url":"https://pith.science/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/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":"2026-08-13T03:26:49Z","links":{"resolver":"https://pith.science/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC","bundle":"https://pith.science/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/bundle.json","state":"https://pith.science/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PGUCWBMEL4ZTY4X6E3SIZN36EC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PGUCWBMEL4ZTY4X6E3SIZN36EC","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":"283fe58fddfb63d766c8548e710ccc6f951d9f4b05b7dd26164c900b34ce17b5","cross_cats_sorted":["cs.LG","math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-11-26T14:40:19Z","title_canon_sha256":"6526843b8594fb5b5e3db15a0552da9fea47b2f8d73d14f630e62ab73deaac05"},"schema_version":"1.0","source":{"id":"2211.14578","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.14578","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"arxiv_version","alias_value":"2211.14578v3","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14578","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_12","alias_value":"PGUCWBMEL4ZT","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_16","alias_value":"PGUCWBMEL4ZTY4X6","created_at":"2026-07-05T07:08:53Z"},{"alias_kind":"pith_short_8","alias_value":"PGUCWBME","created_at":"2026-07-05T07:08:53Z"}],"graph_snapshots":[{"event_id":"sha256:fb42f7a362a6e803e37d251fbc8be2b2989935b3561494bd279d19b52576e779","target":"graph","created_at":"2026-07-05T07:08:53Z","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/2211.14578/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transfer learning has become an essential technique to exploit information from the source domain to boost performance of the target task. Despite the prevalence in high-dimensional data, heterogeneity and heavy tails are insufficiently accounted for by current transfer learning approaches and thus may undermine the resulting performance. We propose a transfer learning procedure in the framework of high-dimensional quantile regression models to accommodate heterogeneity and heavy tails in the source and target domains. We establish error bounds of transfer learning estimator based on delicatel","authors_text":"Jiayu Huang, Mingqiu Wang, Yuanshan Wu","cross_cats":["cs.LG","math.ST","stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-11-26T14:40:19Z","title":"Estimation and inference for transfer learning with high-dimensional quantile regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14578","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":"sha256:4afa3f27422af615f54c09bb67e5ddd53ff717f4f4dd576bc4fa76f465be2604","target":"record","created_at":"2026-07-05T07:08:53Z","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":"283fe58fddfb63d766c8548e710ccc6f951d9f4b05b7dd26164c900b34ce17b5","cross_cats_sorted":["cs.LG","math.ST","stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-11-26T14:40:19Z","title_canon_sha256":"6526843b8594fb5b5e3db15a0552da9fea47b2f8d73d14f630e62ab73deaac05"},"schema_version":"1.0","source":{"id":"2211.14578","kind":"arxiv","version":3}},"canonical_sha256":"79a82b05845f333c72fe26e48cb77e209aa9ecf4b96433bfce2abbbcee1a4465","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79a82b05845f333c72fe26e48cb77e209aa9ecf4b96433bfce2abbbcee1a4465","first_computed_at":"2026-07-05T07:08:53.911706Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:08:53.911706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aMiENaHBCPBKkbqhsd5Dfh75Z2+SHZFZFG/ZgC6iF14j7uKdAr8ykRHwD0C3heafe2MPibVVGvDFtlTaZiiKDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:08:53.912158Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.14578","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4afa3f27422af615f54c09bb67e5ddd53ff717f4f4dd576bc4fa76f465be2604","sha256:fb42f7a362a6e803e37d251fbc8be2b2989935b3561494bd279d19b52576e779"],"state_sha256":"8a39e2fb65f3ae6209b8639a1582073bdeb3b620266e3ec0948ffbf358beb146"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PHrgxM9sYmgh2Eal0YnAmyxZIC2QTlXGSalnlBRJWxFhskK4ibh0hPyrCdwum0s4FPtVdG8N/vpAg7jPfZWIDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T03:26:49.280865Z","bundle_sha256":"2a79e957c0928d6abb27df5b42b7c0ea942573c9b29691205675287af0d82678"}}