{"as_of":"2026-08-15T08:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c7dec32815911c394fae34ca8dd48e27a093de6fd791de07f03fb9898cbd796","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:30:38.186598Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2412.11950/citation-record","integrity":"/paper/2412.11950/integrity","json":"/paper/2412.11950/citation-record.json","paper":"/paper/2412.11950"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:39.069340Z","title":"Asynchronous distributed gaussian process regression,","venue":null,"work_id":"098f3888-2a7c-499b-a6c9-8589ee112471","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.957180Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:2e32767b9b4eeb1dd8a0c46a3d7cb4fa80fa7d3a4264594b0b2d68aabb3f0093","observation_id":"9a9a5bea-892b-4325-835c-9feab6093310","resolution":{"observed_at":"2026-08-11T14:30:39.074815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:39.052971Z","title":"Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning,","venue":null,"work_id":"95a3da5f-8543-4679-9b30-2fe319aafccb","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.963245Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:5dfe64a609e3e7b77f1b789c1d6dfac638d7b9bcdb8fffba2e6e1c4860d541c7","observation_id":"c44218bd-c250-4de1-8d6b-3cab10e16ebf","resolution":{"observed_at":"2026-08-11T14:30:39.058283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:37.969325Z","title":"Sparse gaussian processes using pseudo-inputs,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.969325Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:f538afbf52996c0f64c5a5e5ae4336f7a229655af208fe733d63a34100e4fb9b","observation_id":"92d1266e-ac85-4ef2-be68-80e247ddd36d","resolution":{"observed_at":"2026-08-11T14:30:37.969325Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:37.975017Z","title":"Variational learning of inducing variables in sparse gaussian processes,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.975017Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:3ad3b79fa6f6f2505d41ddf8793c01c6e991031f688252d4b6f27bfd3e76bec0","observation_id":"04d6925a-4140-4d82-9320-86224745f97a","resolution":{"observed_at":"2026-08-11T14:30:37.975017Z","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":"2026-08-11T14:30:39.013779Z","title":"A sparse covariance function for exact gaussian process inference in large datasets,","venue":null,"work_id":"8bd54ab0-8426-4214-94df-364960d2c470","year":2009},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.982232Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:9688f524438ffce10231a82865d2e261bd361f3b06983e545cade6bfdc57b521","observation_id":"b1fe11d9-76e1-4df3-86ec-af785eeedcb2","resolution":{"observed_at":"2026-08-11T14:30:39.019542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.997059Z","title":"A framework for bayesian optimization in embedded subspaces,","venue":null,"work_id":"4d8e1e2f-fb88-486b-b4d3-52c2f3efada6","year":2019},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.988011Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:63d560882889e2e5adfc9eb5a6d4082fc052c1c022ac0f1a5460e78a33d9127c","observation_id":"2d318530-a3cc-4484-bb0c-138a858fa903","resolution":{"observed_at":"2026-08-11T14:30:39.002599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.979929Z","title":"When Gaussian Process Meets Big Data: A Review of Scalable GPs,","venue":null,"work_id":"65ae5fbb-2191-4659-97a6-83251a0323cc","year":2020},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.994113Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:5e17423ac505f8842640689488ee711c7a7b999f65920b6e8345f16d938e50b7","observation_id":"1b12e2f2-a29b-43d8-ba7c-83a03356a178","resolution":{"observed_at":"2026-08-11T14:30:38.985379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.964274Z","title":"Gaussian processes for high- dimensional, large data sets: A review,","venue":null,"work_id":"36e5760d-9369-4627-8b0a-24b99b2cd50e","year":2022},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:37.999219Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:17145260a59b7bb418912833a5bf1f5c601080e97e79369aa555c965f32aa41c","observation_id":"151bde5f-b838-4970-82cf-35fa0e87e9e7","resolution":{"observed_at":"2026-08-11T14:30:38.969086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.948984Z","title":"Mixtures of Gaussian Processes,","venue":null,"work_id":"9aebfb81-ed4c-48e4-be84-61d737d5f395","year":2000},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.004938Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:2fe8b2a3173ea9791b4ba8e66d6d6364ce75b3eb971dd1a42d2e59f5406b4218","observation_id":"efe03f43-4e24-4d4b-85f4-48c31711eb10","resolution":{"observed_at":"2026-08-11T14:30:38.953839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.933153Z","title":"Variational mixture of gaussian process experts,","venue":null,"work_id":"1766ef75-0f87-4695-a239-aa14615428ac","year":2008},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.010350Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:a290a1708df811ef8d5b2fa1c2c9ca640ad2ee433bb21348841477562a7b9119","observation_id":"76d34b33-f617-4fc7-a1a9-bb811a380873","resolution":{"observed_at":"2026-08-11T14:30:38.938091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:38.015659Z","title":"Mixture of Experts: A Literature Survey,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.015659Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:cb00cc4f9015660484312214f3c27cbb5039037e8beec332c8e727ff71c19986","observation_id":"5ca80ccb-ce09-48a8-9681-7ef176afcbd2","resolution":{"observed_at":"2026-08-11T14:30:38.015659Z","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":"2026-08-11T14:30:38.916935Z","title":"Training Products of Experts by Minimizing Contrastive Divergence,","venue":null,"work_id":"9fe54559-42f8-45ba-93a2-a5d640c384e2","year":2002},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.022351Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:c64edbfa1b84a580951cbdb4c6af8acb817ed9f161736bf343a72f08c9b68eb6","observation_id":"78bac765-9f39-467c-a354-636b935cbe63","resolution":{"observed_at":"2026-08-11T14:30:38.922079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.899674Z","title":"Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression,","venue":null,"work_id":"696a02a5-91ed-437e-aaa1-86a1205cb090","year":2014},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.026961Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:b4363f16090c8e88e03c2794ce3b729ac837df53acce9efde0276f8f84132530","observation_id":"c73fe65d-8a18-4871-b990-a57ad6233bb2","resolution":{"observed_at":"2026-08-11T14:30:38.904877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1410.7827","last_updated":"2015-11-24T03:12:57Z","snapshot_observed_at":"2026-08-14T23:13:53.804949Z","submitted_at":"2014-10-28T22:04:30Z","title":"Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1410.7827","snapshot_observed_at":"2026-08-11T14:30:38.032371Z","title":"Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.032371Z"},"links":{"cited_paper":"/paper/1410.7827","citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:d8072b7e6f0923f5dd703056cf66b4913931dddb0ac1fef48a272444e74b7fdd","observation_id":"bf6143f6-d725-4559-b8f1-6c309eb22792","resolution":{"observed_at":"2026-08-11T14:30:38.032371Z","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":"2026-08-11T14:30:38.883009Z","title":"Healing Products of Gaussian Process Experts,","venue":null,"work_id":"0e18dd6f-c671-4550-90b9-c8cbee1daeb1","year":null},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.037932Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:a5e527a79bf5eb3d6e5507fef8d044aeceae32dbc288b15d30f40f299a1a09eb","observation_id":"45d34615-f942-4e1f-a405-5fd11a383b33","resolution":{"observed_at":"2026-08-11T14:30:38.888342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.848975Z","title":"Correlated product of experts for sparse gaussian process regression,","venue":null,"work_id":"74f990e3-6b14-4f43-8d93-4fd3203475be","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.049418Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:932ae9b4cdba9f0e0a25d365545785cc067a31ed9a5f65576fb6a228f006885a","observation_id":"5121344c-9ebf-4316-a874-ffcd956a1054","resolution":{"observed_at":"2026-08-11T14:30:38.854294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.832343Z","title":"A Bayesian committee machine,","venue":null,"work_id":"6c184b98-7740-45a7-a994-1758976d96b9","year":2000},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.055001Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:80ad9eb88c0c17ef4e643df3e877e9ece20c636050aa79eb981a641d9500ef3e","observation_id":"97b8b273-7f23-4a9c-9587-aa2464501a33","resolution":{"observed_at":"2026-08-11T14:30:38.837550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.816855Z","title":"Distributed Gaussian Processes,","venue":null,"work_id":"219a4d6c-47ea-441c-878c-69d95fa62564","year":2015},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.061587Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:e06fd69d97e5d89dce7443608642783b65299889c5eeee593e8b932910c6d597","observation_id":"e30720a5-5d9c-4e4d-a922-63740a2067c2","resolution":{"observed_at":"2026-08-11T14:30:38.821932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.800703Z","title":"Generalized robust bayesian committee machine for large-scale gaussian process regression,","venue":null,"work_id":"ece697ad-b7dc-4d03-8b34-d26e00255550","year":2018},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.066129Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:fad4405bc4981908a4ec57fd848e2f8868f150f07f9e05526a3504092c296351","observation_id":"fa9a821f-b002-4011-8810-5dcd7ef0b22e","resolution":{"observed_at":"2026-08-11T14:30:38.806264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.783959Z","title":"Nested Kriging predictions for datasets with a large number of observations,","venue":null,"work_id":"af76a5e7-98ef-462c-a7ea-f073be76dbf3","year":2018},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.072375Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:630a410992e0a9dd0d43eeec02620dc7f3ff36581b66a5457c114d9d08e79a48","observation_id":"4ef4bbf7-646f-474b-a688-9e37bb29fb1a","resolution":{"observed_at":"2026-08-11T14:30:38.789252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.767260Z","title":"Decentralized nested gaussian pro- cesses for multi-robot systems,","venue":null,"work_id":"4a8ba5a0-1eb7-42c1-bd41-536ff9e13e96","year":2021},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.077712Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:e4b31ac17aa3a0ad8eea32f041197afa68f19f7097dcd60f8fc47da479a25aef","observation_id":"b1526097-f211-4557-89b2-00f78082f598","resolution":{"observed_at":"2026-08-11T14:30:38.772418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.751604Z","title":"Cooperative Learning with Gaussian Processes for Euler- Lagrange Systems Tracking Control Under Switching Topologies,","venue":null,"work_id":"51705920-77ca-4f31-ae86-6a9ff750eb4a","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.082572Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:790a3e164c36a6f8d9ab7fb6b76214b8b59c9d4be04583d8efb7c956ae17cb50","observation_id":"08a88453-e01d-4557-8990-e9c46ef12a48","resolution":{"observed_at":"2026-08-11T14:30:38.756709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.736095Z","title":"Whom to Trust? Elective Learning for Distributed Gaussian Process Regression,","venue":null,"work_id":"d97e35b8-799f-452f-a8f5-1f6937d918fc","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.087784Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:d1cb10821a8d164532efa5436bc62a8ed91b6e2347acb91bf991aa4e6f2f152a","observation_id":"7e8621e1-3e73-48e2-8dc1-c424db8ba191","resolution":{"observed_at":"2026-08-11T14:30:38.741206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.720952Z","title":"Distributed Learning Consensus Control for Unknown Nonlinear Multi- Agent Systems based on Gaussian Processes,","venue":null,"work_id":"f6120a1b-2619-4070-ac09-489e1a036a7f","year":2021},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.092807Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:3fffd9c48bb0b3f1d60b1cd937801c3a7cb36a373176378ef0769e9995e171e6","observation_id":"25b3000e-e641-4eb2-8976-8779cbe70815","resolution":{"observed_at":"2026-08-11T14:30:38.725834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.704912Z","title":"Cooperative Control of Uncertain Multiagent Systems via Distributed Gaussian Processes,","venue":null,"work_id":"17599646-9114-4f9f-9e78-3c258342837b","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.097698Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:fcbd5adf7f5fc4fe8afd39ada21cd5b919c9f48a6e94c435219ba8ee468cca1d","observation_id":"97dce647-2630-46e7-afcd-a488a0bf067c","resolution":{"observed_at":"2026-08-11T14:30:38.709946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.688080Z","title":"Collective online learning of gaussian processes in massive multi-agent systems,","venue":null,"work_id":"aba24b6d-098e-47d2-9888-315a97b4b3c0","year":2019},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.102472Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:81a1830f857ce79635c4c7cb36ed09c694b827d1d355ccd080464fbaf28d05a4","observation_id":"8a5a987b-5e84-4319-8eb7-a785ae3d551d","resolution":{"observed_at":"2026-08-11T14:30:38.693656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.671071Z","title":"Learning- based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy,","venue":null,"work_id":"66cb6a5e-f970-4293-a36a-8a0bd0d1af50","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.107268Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:aa97544c00b939cf97eab6bab1eb7f09074af5dc6064fa04a4bba131c2ba545f","observation_id":"3c8f8984-6936-4e39-82a2-0afda47d66f2","resolution":{"observed_at":"2026-08-11T14:30:38.676458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/1023992","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:38.359276Z","title":"Distributed Online Sparse Gaussian Process Regression for Multi-Agent Coverage Control,","venue":null,"work_id":"5a005c43-9bd2-433c-856b-6c29897ba57a","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.112030Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:9dfef250910f40848c1dce845b863a710c23e57ce0c1d5e34b6e09983af5681d","observation_id":"1d6082bd-1266-445e-aea0-f67e5ea09b98","resolution":{"observed_at":"2026-08-11T14:30:38.369752Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.655038Z","title":"Coop- erative Online Learning for Multiagent System Control via Gaussian Processes With Event-Triggered Mechanism,","venue":null,"work_id":"c080e956-2bf8-41e7-8aed-4306aa30d95b","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.116524Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:4f3aa454298f67a00c60cf7f4af09a8f88d6e6ce0518a020aca1a3079af91e06","observation_id":"0b13cd84-f442-4941-9002-67dc98ce7371","resolution":{"observed_at":"2026-08-11T14:30:38.660104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.638746Z","title":"Decentralized event-triggered online learning for safe consensus control of multi-agent systems with Gaussian process regression,","venue":null,"work_id":"124ce5ee-a1b2-43b8-9a6b-868bc3cf425e","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.121532Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:f43280155f43c4b78654b2a61b7dc23b87f152eca9724a993bd89846320adfe0","observation_id":"b1e79763-8c05-411a-9adc-7bb8e961400d","resolution":{"observed_at":"2026-08-11T14:30:38.644041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.619475Z","title":"Gaussian process decentral- ized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems,","venue":null,"work_id":"8f41b96d-4443-410b-a3e6-de53b68396c3","year":2015},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.126252Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:75bc51d5e996c3409a6378862b81b2941bca3618dbdc1e8cad964cf433fed991","observation_id":"7ed15ae2-705e-4ae9-a6fb-1ba6eae29857","resolution":{"observed_at":"2026-08-11T14:30:38.626002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.594198Z","title":"Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception,","venue":null,"work_id":"e2778c55-b4d5-4fa3-9948-204e0ca4692a","year":2020},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.130998Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:3a093d0fc130942897ffd10a9665d919037c3fb461e8d7b94e842b28f97ff236","observation_id":"05acc5ab-2ba5-4fca-99e7-cf3b09000732","resolution":{"observed_at":"2026-08-11T14:30:38.600841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.576316Z","title":"Decentralized nested gaussian pro- cesses for multi-robot systems,","venue":null,"work_id":"3bfe9d68-0e05-4423-aaef-a29f1ae1f4bc","year":2021},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.135650Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:a5c2488867b1c2ddef68b2cb9f82269081be1acf5a10e4b7361c080e23012c16","observation_id":"826dd3ff-4ebb-4391-abee-9640ae34779e","resolution":{"observed_at":"2026-08-11T14:30:38.581771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.558528Z","title":"Distributed multi-agent gaussian regression via finite-dimensional approximations,","venue":null,"work_id":"f05f7809-deb7-4efc-90a0-1e54db3bfa05","year":2018},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.140476Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:df4e994eae70024dc300e3f1c0c92116285d7ff9584260aa5f9412b4efd045c5","observation_id":"08f99062-7e18-4ab2-aa9a-174e24b79f0e","resolution":{"observed_at":"2026-08-11T14:30:38.564494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.542178Z","title":"Learning of gaussian processes in distributed and communication limited systems,","venue":null,"work_id":"14d03d67-3ea2-4e0c-bac1-5a87b00eb857","year":1928},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.145091Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:9963eea23f32d2d7b7b6f61534198d134e925eb99645e1a3b185b8197911b61b","observation_id":"2e9f1996-29f0-48bc-b3f0-f02651894bf6","resolution":{"observed_at":"2026-08-11T14:30:38.547051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.525719Z","title":"Aggregating dependent gaussian experts in local approximation,","venue":null,"work_id":"7b829c17-84d3-4dec-9c4a-f303eaca8853","year":2020},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.151100Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:8d31f6ff576b536c1595c57aeb6fd31ae9492dc333b5812c6618c0e33a697204","observation_id":"e8fb8e08-2dcb-4266-b83b-8fa76c20914b","resolution":{"observed_at":"2026-08-11T14:30:38.531165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.509716Z","title":"Gaussian processes on graphs via spectral kernel learning,","venue":null,"work_id":"1ffc5107-cbcd-4287-8748-16e51585ffbe","year":2023},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.155779Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:73d69b2ade1672fe5d619e5eb8d4ef9e79c6e6c6af5b290b72b64b0f08aaebfd","observation_id":"505a865f-8900-4f88-b8f0-bd1f3e2f7735","resolution":{"observed_at":"2026-08-11T14:30:38.514650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:30:38.160373Z","title":"Mobile sensor network navigation using gaussian processes with truncated observations,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.160373Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:c4a9406ef9cd8fff3bea7db5648749a2d5b28c1ce877f9cc3260c207eec73565","observation_id":"54200e15-b552-4aae-a45e-894327f2857f","resolution":{"observed_at":"2026-08-11T14:30:38.160373Z","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":"2026-08-11T14:30:38.474879Z","title":"Multi-Robot Active Sensing and Environmental Model Learning With Distributed Gaussian Process,","venue":null,"work_id":"dcacc318-fe20-4f0a-9635-65d8f3c22dcb","year":2020},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.164724Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:1ca5760d17ed133dd6bf1519a41f88857c39350af8fd4e60e483b7a4d2c3a3db","observation_id":"760487cf-3ca7-497a-9aae-03c190ade021","resolution":{"observed_at":"2026-08-11T14:30:38.481989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.452416Z","title":"Adaptive Sampling and Online Learning in Multi-Robot Sensor Coverage with Mixture of Gaussian Processes,","venue":null,"work_id":"250914c2-029a-4e62-bec3-2809187abeaf","year":2018},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.170213Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:ea565122ae800dece7c417661d1be58d36e621a825b5290b9c3bbc0fa08d082d","observation_id":"8ba2d570-372b-4fb2-86f3-5dcea8df976c","resolution":{"observed_at":"2026-08-11T14:30:38.458866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.434378Z","title":"Resource-Efficient Coop- erative Online Scalar Field Mapping Via Distributed Sparse Gaussian Process Regression,","venue":null,"work_id":"593cc23a-030e-49d2-b64e-95cd357fa2ee","year":2024},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.175215Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:14e542607b502081b60b41863834fda3bf8d10ef318a3994a69debb631ef069c","observation_id":"39473f47-f856-44d7-aa11-8c7d1f89d66b","resolution":{"observed_at":"2026-08-11T14:30:38.440370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.417771Z","title":"Learning-based Symbolic Abstractions for Nonlinear Control Systems,","venue":null,"work_id":"d81ef339-6db8-44e7-993f-26d19a2ed292","year":2022},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.181048Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:f2c63afb4ef01a2cfdc8c3ec60564b14374dd72eea9a8b02b94c473cee8308e4","observation_id":"d0d9d228-a49d-4e8d-b803-ac3c80c52686","resolution":{"observed_at":"2026-08-11T14:30:38.423183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.400627Z","title":null,"venue":null,"work_id":"bf42e91e-aa20-4e67-a4ad-71dedd90db07","year":2009},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.186598Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:c1953a2a51028442f75330c9813b9da95b0f605d7487f57ceb992de197eeb524","observation_id":"e2b9aeb6-c285-48c0-ae54-282ab4e8aecf","resolution":{"observed_at":"2026-08-11T14:30:38.405946Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T14:30:38.865117Z","title":"2068–2077","venue":null,"work_id":"90c829f8-8ec3-474d-b646-19d7dfd164eb","year":2020},"citing_paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document","version":1},"reference_index":119,"source":"pdf_text","source_observed_at":"2026-08-11T14:30:38.043888Z"},"links":{"citing_paper":"/paper/2412.11950"},"observation_digest":"sha256:69f7dbe89b3da544a4b7281621cd64a35a96d5c10db0706dc92f6b8505217ecc","observation_id":"21f4ba58-da34-4a7d-a334-be2418616ef2","resolution":{"observed_at":"2026-08-11T14:30:38.870219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11950","last_updated":"2024-12-16T16:34:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-11T14:23:30.407793Z","submitted_at":"2024-12-16T16:34:48Z","title":"Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":37},"total_outbound_references":44},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.11950."}