{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:7PMEINQYRFGKTHIBNMBLK6UKAK","short_pith_number":"pith:7PMEINQY","canonical_record":{"source":{"id":"2404.09306","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-04-14T17:08:43Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"cda2c90c4119751f09d468ae052a5fd9d63a9d1a8b6242919b55ef7a5f5fd565","abstract_canon_sha256":"509792065de1c5882767bf049d269f133423b322f161a902506e83c1222fcbf6"},"schema_version":"1.0"},"canonical_sha256":"fbd8443618894ca99d016b02b57a8a02811f6afae49eaa23d4e61a34429dca66","source":{"kind":"arxiv","id":"2404.09306","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.09306","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"arxiv_version","alias_value":"2404.09306v1","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.09306","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_12","alias_value":"7PMEINQYRFGK","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_16","alias_value":"7PMEINQYRFGKTHIB","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_8","alias_value":"7PMEINQY","created_at":"2026-07-05T08:08:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:7PMEINQYRFGKTHIBNMBLK6UKAK","target":"record","payload":{"canonical_record":{"source":{"id":"2404.09306","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-04-14T17:08:43Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"cda2c90c4119751f09d468ae052a5fd9d63a9d1a8b6242919b55ef7a5f5fd565","abstract_canon_sha256":"509792065de1c5882767bf049d269f133423b322f161a902506e83c1222fcbf6"},"schema_version":"1.0"},"canonical_sha256":"fbd8443618894ca99d016b02b57a8a02811f6afae49eaa23d4e61a34429dca66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:08:04.561724Z","signature_b64":"9VDxUdKzBrfub5uHfM+JHOOjCiGH8+yGnVHFxj9MtmovwgfrBKieSsRBBDNBsQ6EgYm3HHUDx3xzv8dLVe3uBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbd8443618894ca99d016b02b57a8a02811f6afae49eaa23d4e61a34429dca66","last_reissued_at":"2026-07-05T08:08:04.561297Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:08:04.561297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.09306","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":"2026-07-05T08:08:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VWjQGSNsYjIjlIjdkOsc8DTFTaIMIerLaTYBcfNhbhLckTrl8IfbJmnSbBf1VUnjb1m3Ft9KRPWtH52DfewsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T02:44:58.298623Z"},"content_sha256":"2a79df31bbabbdc542955ce1ad997f8c38999c943dc8065b186630522bb62454","schema_version":"1.0","event_id":"sha256:2a79df31bbabbdc542955ce1ad997f8c38999c943dc8065b186630522bb62454"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:7PMEINQYRFGKTHIBNMBLK6UKAK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Cecile Durot, Debarghya Mukherjee","submitted_at":"2024-04-14T17:08:43Z","abstract_excerpt":"Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regression, multi-target tracking problems, image denoising, etc. However, a notable gap exists in the existing literature, particularly in vanishing noise, i.e., how the rate of estimation of the underlying signal scales with the error variance. This paper aims to bridge this gap by delving into the monotone function estimation problem under vanishing noise variance, i.e., we allow the error variance to go to $0$ as the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.09306","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/2404.09306/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":"2026-07-05T08:08:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QLr/7p5d4s2IsHV4pPA+q+W7b0PrJOjYglUSWQKD3mS2lgevqggzMzsnj5KalCCGonapQP61/SKVI3XOechTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T02:44:58.299138Z"},"content_sha256":"303c458fc00b99c46697778b96ff3802b5ac51cc9a0c8527e90f8c50c6df1109","schema_version":"1.0","event_id":"sha256:303c458fc00b99c46697778b96ff3802b5ac51cc9a0c8527e90f8c50c6df1109"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/bundle.json","state_url":"https://pith.science/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/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":"2026-08-22T02:44:58Z","links":{"resolver":"https://pith.science/pith/7PMEINQYRFGKTHIBNMBLK6UKAK","bundle":"https://pith.science/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/bundle.json","state":"https://pith.science/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7PMEINQYRFGKTHIBNMBLK6UKAK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:7PMEINQYRFGKTHIBNMBLK6UKAK","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":"509792065de1c5882767bf049d269f133423b322f161a902506e83c1222fcbf6","cross_cats_sorted":["stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-04-14T17:08:43Z","title_canon_sha256":"cda2c90c4119751f09d468ae052a5fd9d63a9d1a8b6242919b55ef7a5f5fd565"},"schema_version":"1.0","source":{"id":"2404.09306","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.09306","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"arxiv_version","alias_value":"2404.09306v1","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.09306","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_12","alias_value":"7PMEINQYRFGK","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_16","alias_value":"7PMEINQYRFGKTHIB","created_at":"2026-07-05T08:08:04Z"},{"alias_kind":"pith_short_8","alias_value":"7PMEINQY","created_at":"2026-07-05T08:08:04Z"}],"graph_snapshots":[{"event_id":"sha256:303c458fc00b99c46697778b96ff3802b5ac51cc9a0c8527e90f8c50c6df1109","target":"graph","created_at":"2026-07-05T08:08:04Z","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/2404.09306/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regression, multi-target tracking problems, image denoising, etc. However, a notable gap exists in the existing literature, particularly in vanishing noise, i.e., how the rate of estimation of the underlying signal scales with the error variance. This paper aims to bridge this gap by delving into the monotone function estimation problem under vanishing noise variance, i.e., we allow the error variance to go to $0$ as the n","authors_text":"Cecile Durot, Debarghya Mukherjee","cross_cats":["stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-04-14T17:08:43Z","title":"Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.09306","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":"sha256:2a79df31bbabbdc542955ce1ad997f8c38999c943dc8065b186630522bb62454","target":"record","created_at":"2026-07-05T08:08:04Z","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":"509792065de1c5882767bf049d269f133423b322f161a902506e83c1222fcbf6","cross_cats_sorted":["stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-04-14T17:08:43Z","title_canon_sha256":"cda2c90c4119751f09d468ae052a5fd9d63a9d1a8b6242919b55ef7a5f5fd565"},"schema_version":"1.0","source":{"id":"2404.09306","kind":"arxiv","version":1}},"canonical_sha256":"fbd8443618894ca99d016b02b57a8a02811f6afae49eaa23d4e61a34429dca66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbd8443618894ca99d016b02b57a8a02811f6afae49eaa23d4e61a34429dca66","first_computed_at":"2026-07-05T08:08:04.561297Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:08:04.561297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9VDxUdKzBrfub5uHfM+JHOOjCiGH8+yGnVHFxj9MtmovwgfrBKieSsRBBDNBsQ6EgYm3HHUDx3xzv8dLVe3uBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:08:04.561724Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.09306","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a79df31bbabbdc542955ce1ad997f8c38999c943dc8065b186630522bb62454","sha256:303c458fc00b99c46697778b96ff3802b5ac51cc9a0c8527e90f8c50c6df1109"],"state_sha256":"a3cf4fed3593127ffb9852098f0ae41f35b78b13d7f21802f3676f89c5489b96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yyII+F4zxM3fCBFECePc6ku+P+AONcEJDJOmp7XZ1F9lMANPkjMkUnlruCKaE09yBUF+3VaC0dLHRvH7u725Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T02:44:58.302773Z","bundle_sha256":"03f04e69e11a79396a990e1dc3c8e17000e86c61e0c12f9dcaaf63cac2fd2b2c"}}