{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:75FOXQR5LTKLRD534266TQDATV","short_pith_number":"pith:75FOXQR5","canonical_record":{"source":{"id":"1912.12213","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-12-27T16:13:21Z","cross_cats_sorted":["econ.EM","stat.ML","stat.TH"],"title_canon_sha256":"bd2b497f76ff35f95a7c515ccf61a824c195c8c8acd09d29348806f17fd0b457","abstract_canon_sha256":"4016a657ba16a13685c195f52278533635e6bf4bd0787009a693ef603c6b85cc"},"schema_version":"1.0"},"canonical_sha256":"ff4aebc23d5cd4b88fbbe6bde9c0609d66aaeeb2a06eb686f40cb7a2f86c2cb7","source":{"kind":"arxiv","id":"1912.12213","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.12213","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"1912.12213v7","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.12213","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"75FOXQR5LTKL","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"75FOXQR5LTKLRD53","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"75FOXQR5","created_at":"2026-07-05T11:46:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:75FOXQR5LTKLRD534266TQDATV","target":"record","payload":{"canonical_record":{"source":{"id":"1912.12213","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-12-27T16:13:21Z","cross_cats_sorted":["econ.EM","stat.ML","stat.TH"],"title_canon_sha256":"bd2b497f76ff35f95a7c515ccf61a824c195c8c8acd09d29348806f17fd0b457","abstract_canon_sha256":"4016a657ba16a13685c195f52278533635e6bf4bd0787009a693ef603c6b85cc"},"schema_version":"1.0"},"canonical_sha256":"ff4aebc23d5cd4b88fbbe6bde9c0609d66aaeeb2a06eb686f40cb7a2f86c2cb7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:46:31.695565Z","signature_b64":"ydmlTtx7gS6qRYdIHG9GG25KZ2vFeVz9+7bwZmyOFA7WHK2o6TUnKXLQvoJQvtI5q0P5qx7fASvZUNsCnYDECg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff4aebc23d5cd4b88fbbe6bde9c0609d66aaeeb2a06eb686f40cb7a2f86c2cb7","last_reissued_at":"2026-07-05T11:46:31.695050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:46:31.695050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.12213","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SHkCY7HYgR4+h+5AhM7pGIs8l/mOMiKWlBrB01Bt3961UOgtlxXlMxb5P2da9WCP6oz3qcgWDcA1i85xSO6ZAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:46:59.781029Z"},"content_sha256":"75117d06fd0aed531f5fde982634edc01fa0481eb64d70ce99682c041f1156ed","schema_version":"1.0","event_id":"sha256:75117d06fd0aed531f5fde982634edc01fa0481eb64d70ce99682c041f1156ed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:75FOXQR5LTKLRD534266TQDATV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Minimax Semiparametric Learning With Approximate Sparsity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Jelena Bradic, Victor Chernozhukov, Whitney K. Newey, Yinchu Zhu","submitted_at":"2019-12-27T16:13:21Z","abstract_excerpt":"Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of exact model sparsity, meaning that only a small number of parameters are precisely non-zero. This excludes models where linear formulations only approximate the underlying data distribution, such as nonparametric regression methods that use basis expansion such as splines, kernel methods or polynomial regressions. Many recent methods for root-$n$ estimation have been proposed, but the implications of exact model spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.12213","kind":"arxiv","version":7},"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/1912.12213/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":"2026-07-05T11:46:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gl51wEGEWshzJK+6KP8BKJexJrFfgovQSn1yt7CG5bzx7pHZUTA15gASZm54ztYuoMHcl9xEiVSzqc99RtGhBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T22:46:59.781962Z"},"content_sha256":"b648f1607cad4ac166b36b4d794177866f071a55ca02174e3863a1bdef2100ef","schema_version":"1.0","event_id":"sha256:b648f1607cad4ac166b36b4d794177866f071a55ca02174e3863a1bdef2100ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/75FOXQR5LTKLRD534266TQDATV/bundle.json","state_url":"https://pith.science/pith/75FOXQR5LTKLRD534266TQDATV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/75FOXQR5LTKLRD534266TQDATV/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":"2026-08-10T22:46:59Z","links":{"resolver":"https://pith.science/pith/75FOXQR5LTKLRD534266TQDATV","bundle":"https://pith.science/pith/75FOXQR5LTKLRD534266TQDATV/bundle.json","state":"https://pith.science/pith/75FOXQR5LTKLRD534266TQDATV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/75FOXQR5LTKLRD534266TQDATV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:75FOXQR5LTKLRD534266TQDATV","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":"4016a657ba16a13685c195f52278533635e6bf4bd0787009a693ef603c6b85cc","cross_cats_sorted":["econ.EM","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-12-27T16:13:21Z","title_canon_sha256":"bd2b497f76ff35f95a7c515ccf61a824c195c8c8acd09d29348806f17fd0b457"},"schema_version":"1.0","source":{"id":"1912.12213","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.12213","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"arxiv_version","alias_value":"1912.12213v7","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.12213","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_12","alias_value":"75FOXQR5LTKL","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_16","alias_value":"75FOXQR5LTKLRD53","created_at":"2026-07-05T11:46:31Z"},{"alias_kind":"pith_short_8","alias_value":"75FOXQR5","created_at":"2026-07-05T11:46:31Z"}],"graph_snapshots":[{"event_id":"sha256:b648f1607cad4ac166b36b4d794177866f071a55ca02174e3863a1bdef2100ef","target":"graph","created_at":"2026-07-05T11:46:31Z","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/1912.12213/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of exact model sparsity, meaning that only a small number of parameters are precisely non-zero. This excludes models where linear formulations only approximate the underlying data distribution, such as nonparametric regression methods that use basis expansion such as splines, kernel methods or polynomial regressions. Many recent methods for root-$n$ estimation have been proposed, but the implications of exact model spa","authors_text":"Jelena Bradic, Victor Chernozhukov, Whitney K. Newey, Yinchu Zhu","cross_cats":["econ.EM","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-12-27T16:13:21Z","title":"Minimax Semiparametric Learning With Approximate Sparsity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.12213","kind":"arxiv","version":7},"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":"sha256:75117d06fd0aed531f5fde982634edc01fa0481eb64d70ce99682c041f1156ed","target":"record","created_at":"2026-07-05T11:46:31Z","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":"4016a657ba16a13685c195f52278533635e6bf4bd0787009a693ef603c6b85cc","cross_cats_sorted":["econ.EM","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-12-27T16:13:21Z","title_canon_sha256":"bd2b497f76ff35f95a7c515ccf61a824c195c8c8acd09d29348806f17fd0b457"},"schema_version":"1.0","source":{"id":"1912.12213","kind":"arxiv","version":7}},"canonical_sha256":"ff4aebc23d5cd4b88fbbe6bde9c0609d66aaeeb2a06eb686f40cb7a2f86c2cb7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ff4aebc23d5cd4b88fbbe6bde9c0609d66aaeeb2a06eb686f40cb7a2f86c2cb7","first_computed_at":"2026-07-05T11:46:31.695050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:46:31.695050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ydmlTtx7gS6qRYdIHG9GG25KZ2vFeVz9+7bwZmyOFA7WHK2o6TUnKXLQvoJQvtI5q0P5qx7fASvZUNsCnYDECg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:46:31.695565Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.12213","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75117d06fd0aed531f5fde982634edc01fa0481eb64d70ce99682c041f1156ed","sha256:b648f1607cad4ac166b36b4d794177866f071a55ca02174e3863a1bdef2100ef"],"state_sha256":"66f65cba4f0607c06576ec7b8e81a6a38b34daf8256158053a1750bdeb50a7b9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DL2PLt0b/GyXSx/v3sQwL39nSEtZeRbuHRzoFbehZ3LpNBDGGMkHqE6ugti6gkJa8KKCvtHWev20yqUjye9HAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T22:46:59.788002Z","bundle_sha256":"4230fd5556416b3475deb6c8bf40aa676052a92a46772a28f18a1c35c73eca39"}}