{"as_of":"2026-08-13T10:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:336d0a0cf30cc7c7002f8794198f50d9625434143a40a29fd39c8b3c0703fc03","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:45:23.667555Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.07879/citation-record","integrity":"/paper/2501.07879/integrity","json":"/paper/2501.07879/citation-record.json","paper":"/paper/2501.07879"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.131401Z","title":"Communication-efficient learning of deep networks fr om decentralized data,","venue":null,"work_id":"bf356092-5902-4a57-9b7c-2e88e7c15639","year":2017},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.551542Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:aaeff23c0e3c4a4931d205512373e7b80d736b7ea40e5e0d41ea21968ea367c7","observation_id":"6fac4873-c100-4d4b-9d15-0339ff4c05d8","resolution":{"observed_at":"2026-08-10T20:45:24.136258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.118143Z","title":"Federated l earning: Challenges, methods, and future directions,","venue":null,"work_id":"8ae2acb0-576a-4a95-8a4b-f7f8c3b8a815","year":2020},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.557144Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:24c89c1c74b0970676e097d8fe5d50dffcf54e4194eb966edacd9f7ace24c814","observation_id":"e978d60e-0074-4a50-afb7-a7f3bf0f4df4","resolution":{"observed_at":"2026-08-10T20:45:24.122607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.103842Z","title":"Advances and open problems in federa ted learning,","venue":null,"work_id":"1356b1b9-0a97-434c-b5d7-096076c7c1cf","year":2021},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.561534Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:65773b56d5a395e6892c16565caa492dbbc5ac89fc142d714e7e974362c6bd83","observation_id":"fe07494c-d4de-4fb2-97f6-153e747f4906","resolution":{"observed_at":"2026-08-10T20:45:24.108553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.10373","last_updated":"2022-04-21T19:04:50Z","snapshot_observed_at":"2026-08-11T14:45:32.219980Z","submitted_at":"2022-04-21T19:04:50Z","title":"Distributed Nonparametric Estimation under Communication Constraints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.10373","snapshot_observed_at":"2026-08-10T20:45:23.566199Z","title":"Distributed nonparametric estim ation under communication constraints,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.566199Z"},"links":{"cited_paper":"/paper/2204.10373","citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:527116e8d3cd1038a312d28cce9879a2ea67a094fa13efaace9a0f9661ad7a3f","observation_id":"9e6e7ff0-2015-4294-b8ad-b033fb605d1b","resolution":{"observed_at":"2026-08-10T20:45:23.566199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.087583Z","title":"Adaptive distributed method s under communication constraints,","venue":null,"work_id":"65f94309-d488-4c5f-9cee-e9b48b5bb6a2","year":2020},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.571861Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:0a58007ba91dbf45d9cbcff926d8ccba5f1a10ecda3385284dae2607aeec6b86","observation_id":"38a6d8c5-947c-492b-a436-0f8e18f7ab97","resolution":{"observed_at":"2026-08-10T20:45:24.093189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.073189Z","title":"Lower bounds for learn ing distributions under communication constraints via fish er information,","venue":null,"work_id":"c9d4d1ac-b8bd-4d98-a9b3-d1a947b4267e","year":2020},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.577000Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:4fb816651901917776409bad19439d0b177b5c7e73b33af7e86fc07a5a3b7ac5","observation_id":"c467fe1c-fbfa-441a-8038-3489533f9f46","resolution":{"observed_at":"2026-08-10T20:45:24.078145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.056436Z","title":"Opti mal rates for nonparametric density estimation under commu nication constraints,","venue":null,"work_id":"375f2ba8-73a2-45bf-9d25-11ae39c04ac6","year":1939},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.582434Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:7fe958e55d57d4add7c218a06ed4e3770f7f3abd8c04cbf1f7c389b18f10543d","observation_id":"960e59de-b1c5-4045-806a-5cdfb6bdc06a","resolution":{"observed_at":"2026-08-10T20:45:24.060960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06884","last_updated":"2024-11-08T16:02:20Z","snapshot_observed_at":"2026-08-12T22:27:15.621370Z","submitted_at":"2024-10-09T13:46:08Z","title":"Adaptive Refinement Protocols for Distributed Distribution Estimation under $\\ell^p$-Losses","version":2},"cited_work":{"arxiv_id":"2410.06884","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.06884","snapshot_observed_at":"2026-08-10T20:45:23.726887Z","title":"Adaptive Refinement Protocols for Distributed Distribution Estimation under $\\ell^p$-Losses","venue":"cs.LG","work_id":"0d89782b-ec09-4163-86a3-26709a637e5b","year":2024},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.586684Z"},"links":{"cited_paper":"/paper/2410.06884","citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:0b8dd9b1ac8568d73f34f3a2164bca7fcff6669239f971f16b2b0cb8e5bcbb8e","observation_id":"e3609e9d-0f0c-473f-a49b-c8460db2bb47","resolution":{"observed_at":"2026-08-10T20:45:23.734849Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.042112Z","title":"In teractive inference under information constraints,","venue":null,"work_id":"db1a5821-edc0-41b7-a0ea-6c2afc1a438e","year":2022},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.591512Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:eeb4e52b12996f2db56dc4a774716f47db01c26d4379c0075dad6de6de636b5c","observation_id":"00f6a534-55ee-44bc-8456-d5c3bf8d513f","resolution":{"observed_at":"2026-08-10T20:45:24.046502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.020037Z","title":"Unified l ower bounds for interactive high-dimensional estimation u nder information constraints,","venue":null,"work_id":"b3d95859-1c09-4991-97d2-91c0b47aeff9","year":2023},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.596060Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:6e63dde8800a2438365d8e48ff6631951a79deb986dfb8b970a1a64fd8e91394","observation_id":"19158fb7-6712-4025-896b-e499688dafac","resolution":{"observed_at":"2026-08-10T20:45:24.031402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:24.000593Z","title":"Distributed nonparametric reg ression under communication constraints,","venue":null,"work_id":"bcf4b247-1a02-453a-90bb-f499dcf2dfca","year":2018},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.600327Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:9c38d8590c915e5368d1d1bf5cedf6bf494090fbcc252b5b27a0604bc139c4da","observation_id":"9716c780-6640-459b-90e3-5da99d0e254d","resolution":{"observed_at":"2026-08-10T20:45:24.006362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.980405Z","title":"Distributed function estim ation: Adaptation using minimal communication,","venue":null,"work_id":"b7aaba4e-1749-413e-8890-98e132cdf279","year":2022},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.606146Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:2a98b5fbc068aa821ef2c70a61b03032d54017617304a0f0e83e381e44c79618","observation_id":"7eb72a8e-8e56-4414-af8b-66b2445d23e1","resolution":{"observed_at":"2026-08-10T20:45:23.986129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.961561Z","title":"Distributed nonparametric functi on estimation: Optimal rate of convergence and cost of adapt ation,","venue":null,"work_id":"8294cd90-13b1-4620-b73c-d7409d802bcb","year":2022},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.610981Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:aed99d0bbc945f32b7a82700eb899d27a90369c7121688d31ce120b477f31113","observation_id":"d19316c0-c595-41c2-8d16-32a8cab62db1","resolution":{"observed_at":"2026-08-10T20:45:23.967716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.943560Z","title":"Local d ifferential privacy: Elbow effect in optimal density estim ation and adaptation over Besov ellipsoids,","venue":null,"work_id":"c1d0c5b4-12e9-4449-83e0-45fc45a1598b","year":2020},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.615803Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:d8186bac63ce88ddf0c28f9f7b6bfcecb674dcca4a5cebcf7ce4970bf1968783","observation_id":"e002a442-910d-4689-a225-ee3d701c72a6","resolution":{"observed_at":"2026-08-10T20:45:23.949124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.929713Z","title":"On density estimation at a fixed point under lo cal differential privacy,","venue":null,"work_id":"f96452eb-05ff-4f5d-8a53-30cb2dab75c0","year":2021},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.620557Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:13e29eb65bed14e0f759fb1ab0d1ae7d7adedea33ada80b7d9ff0ebf6b351430","observation_id":"dcc58572-2e3b-42ff-bd4f-53bdd461470a","resolution":{"observed_at":"2026-08-10T20:45:23.934412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.911177Z","title":"Density estimation under local differential privacy and Hellinger loss,","venue":null,"work_id":"2ebc1f42-5bf7-46ed-a09e-543a5eef3d6f","year":2023},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.625627Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:71a2b6a1dc95c6b7fdc37a3ca01765fb382d26d0015c77366f392c45cf24ada9","observation_id":"40044d14-15bc-42f2-8703-6ca3cf8d2057","resolution":{"observed_at":"2026-08-10T20:45:23.917708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.895555Z","title":"About the co st of central privacy in density estimation,","venue":null,"work_id":"b0ae8606-d294-48ce-a1e7-94df8053f787","year":2023},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.629783Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:3e0fdcad1c74c4154064df5a9716d2742999ef30425d0f0b7c1b60b234b117b6","observation_id":"df757f1d-8d08-4b8b-acde-65a204f9cd60","resolution":{"observed_at":"2026-08-10T20:45:23.900155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06755","last_updated":"2024-06-10T19:34:07Z","snapshot_observed_at":"2026-08-12T23:46:06.432267Z","submitted_at":"2024-06-10T19:34:07Z","title":"Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06755","snapshot_observed_at":"2026-08-10T20:45:23.633594Z","title":"Optimal fe derated learning for nonparametric regression with hetero geneous distributed differential privacy constraints,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.633594Z"},"links":{"cited_paper":"/paper/2406.06755","citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:bfb5246cc0835e55636fb73935aceb13d437dffde503a06c3044bcb47ac3a13f","observation_id":"56b16d26-a167-42fc-9d16-ea4e0a79ecf2","resolution":{"observed_at":"2026-08-10T20:45:23.633594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.877930Z","title":"Communication complexity of two-party nonpar ametric global density estimation,","venue":null,"work_id":"7c874bca-b262-4908-a512-aab39a66fe94","year":2022},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.638367Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:617970b39a84b8f80f3f625b064ca16f0680628ed0f772fcc3ef9761b2d99ee2","observation_id":"163105b9-84fd-47fa-8ada-324c7ed6bbca","resolution":{"observed_at":"2026-08-10T20:45:23.883991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.863065Z","title":"A few interactions improve distributed nonparame tric estimation, optimally,","venue":null,"work_id":"74995976-6604-4262-9de7-fbab6ed027a6","year":2023},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.642562Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:31d3ca545bd1b027bf3a36a138d16fcb99f268f1c6285f4daa56a516399b26ad","observation_id":"ff76f0d2-0965-4955-8603-b4c59f7ad274","resolution":{"observed_at":"2026-08-10T20:45:23.868282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.848751Z","title":"Giné and R","venue":null,"work_id":"1652fdc7-a964-4c16-b95d-8d9036218f2d","year":null},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.646753Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:3e9f4879b4bb0a59a91c4638b5b77608ae9af48adc92261931e50ba5efc90ad1","observation_id":"ece08040-f58e-4371-a921-67350b62bb73","resolution":{"observed_at":"2026-08-10T20:45:23.853091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.833806Z","title":null,"venue":null,"work_id":"1af9862c-e204-484a-a0ba-fd4cd8e29b6d","year":2019},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.651155Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:365a08ca921106c026b17f53d8c030612782118b8a14332a8d5eb24766faa1d6","observation_id":"5bb60fe2-55fb-412b-8aa7-3d4bbba4bc80","resolution":{"observed_at":"2026-08-10T20:45:23.838120Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.818601Z","title":"Dis tributed estimation with multiple samples per user: Sharp r ates and phase transition,","venue":null,"work_id":"eddfdc2c-a893-4ff3-912e-9dbadcbc7ed0","year":2021},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.655214Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:a25ee14fd597aef109c0b575aef2e4a0df536169a72dd55458a74e312b9d7337","observation_id":"0bf05176-1fb2-4a1d-b7a4-9b1ac5785f3c","resolution":{"observed_at":"2026-08-10T20:45:23.823479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.797763Z","title":"Daubechies, Ten Lectures on W avelets","venue":null,"work_id":"355900a0-f425-4b5d-8962-aa921b1f6b70","year":1992},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.659381Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:17cd0490117b66cf6c712cc50f3debe62959c489ec4e28b39fb38dbbefeb3c33","observation_id":"899e3220-52f9-4dd0-8def-2ecb466787b6","resolution":{"observed_at":"2026-08-10T20:45:23.804093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.778795Z","title":"Sobolev, Besov and Nikolskii fractional spa ces: Imbeddings and comparisons for vector valued spaces on an interval,","venue":null,"work_id":"3ca4cec4-0f73-4b0d-a8e6-092b2fdd91e8","year":1990},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.663302Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:e3ae6393d17580e8fc16fefc4f471b5ab196ade9acdd68b4b0f4c4edd56c87d1","observation_id":"28babcb4-04cf-4c13-8775-92f5ff66424b","resolution":{"observed_at":"2026-08-10T20:45:23.785603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:45:23.761711Z","title":"Inference unde r information constraints II: Communication constraints a nd shared randomness,","venue":null,"work_id":"1d300bf0-dcad-4e36-87d0-b31172243692","year":2020},"citing_paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:45:23.667555Z"},"links":{"citing_paper":"/paper/2501.07879"},"observation_digest":"sha256:05d8a0984243eb9918d4fd728d3208671e3c9ebc98b1da33b7571fe99f0694fa","observation_id":"d1d40913-c294-4e73-a066-1f3daffd52e5","resolution":{"observed_at":"2026-08-10T20:45:23.766814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.07879","last_updated":"2025-01-14T06:41:55Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T14:45:57.335504Z","submitted_at":"2025-01-14T06:41:55Z","title":"Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":22},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2501.07879."}