{"as_of":"2026-08-21T09:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cf8467f97834f1c3a2dcbac0958cee49bd171254b59e8432cfd3c95c63347b96","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":"2026-05-24T19:42:43.916120Z","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":"2026-08-21T06:32:19.484+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/1907.08087/citation-record","integrity":"/paper/1907.08087/integrity","json":"/paper/1907.08087/citation-record.json","paper":"/paper/1907.08087"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bayesian Reasoning and Machine Learning","venue":null,"work_id":"b3dc4af5-667f-450e-9255-c2bf117e0591","year":2012},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:354edec368568d452ccad2f1ca31e0dcc949554968bd1d8742e26c153d760325","observation_id":"aa369e6d-c32c-4989-a2a4-0996c2dd7329","resolution":{"observed_at":"2026-05-24T19:56:22.226532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1601.08068","last_updated":"2017-08-15T22:08:11Z","snapshot_observed_at":"2026-08-18T13:31:13.442038Z","submitted_at":"2016-01-29T11:55:26Z","title":"System Identification through Online Sparse Gaussian Process Regression with Input Noise","version":3},"cited_work":{"arxiv_id":"1601.08068","doi":null,"metadata_source":"pith","pith_arxiv_id":"1601.08068","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"System Identification through Online Sparse Gaussian Process Regression with Input Noise","venue":"stat.ML","work_id":"fd9c9c97-7efc-4c1a-b047-e01b8a645de2","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"cited_paper":"/paper/1601.08068","citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:e8dc8ccc616cab60157e354cd2cff81c8330560ba1ddd22b129b36e6d54281e7","observation_id":"cdf87b21-7d09-4151-acda-906b38198bdf","resolution":{"observed_at":"2026-05-24T19:44:53.406853Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A survey on multi-output regression.Wiley Int","venue":null,"work_id":"455ac5cd-b606-4fec-af91-6ee5a6db8839","year":2015},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:a5352b2da181e82f434511d36784524739df5c2b5feceaf60978e54fa769df01","observation_id":"b14b4211-2f2d-4a69-ac95-38e9e7cbd4ec","resolution":{"observed_at":"2026-05-24T19:56:22.222896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Bugallo, Luca Martino, and Jukka Corander","venue":null,"work_id":"16df1b5a-6861-4d3b-afb4-70a5af3b62a1","year":2015},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:6b0adde69d424f1aea62a02fe697d8daf1581d46d14f7593a9014346417053b5","observation_id":"3be54eda-b717-402a-96c1-9907f6ae1aaa","resolution":{"observed_at":"2026-05-24T19:56:22.237457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Adios: Archi- tectures deep in output space","venue":null,"work_id":"b3de1599-94a5-4c5c-8bfa-ec8a22f44aa5","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:e0697c317e0479972f83103d67a587e3ba045d7c6e968a97b001f2514b99281b","observation_id":"25bd21eb-3e44-4917-a9a1-f7ed4fc31ff7","resolution":{"observed_at":"2026-05-24T19:56:22.230503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"An approximate inference with Gaussian Process to latent functions from uncertain data","venue":null,"work_id":"0c0c4ab8-cc22-45b3-b7e0-20a00c47526f","year":1945},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:3b9ce4470df27598ab2f91119b290ae1100cd5ef14cfff73d7b9612641e816b2","observation_id":"e66eea95-b829-4ce4-8a45-77577d69311f","resolution":{"observed_at":"2026-05-24T19:56:22.219568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Deep Gaussian Processes","venue":null,"work_id":"407e13fa-1bc2-46ac-a189-abc41dc35571","year":2013},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:34dfa0a1cd05a81f449eb520e5f783ac8892fa0dfa20b4cc7c0a9f57e16c9cb5","observation_id":"96e9e08a-c30f-444e-9efb-43838cea55e2","resolution":{"observed_at":"2026-05-24T19:56:22.233960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ce9e7b84-1def-4c69-b405-26f449275f9a","year":2009},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:c8ca1eee0c860116dcd6eb10582325b83f948ffecc0f3e4048a36b67699fdb18","observation_id":"a8e8f5b7-ab3a-4475-b2ba-0eeef7b82220","resolution":{"observed_at":"2026-05-24T19:56:22.184708Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Dellaportas and D","venue":null,"work_id":"9b8da3ee-3f15-4f59-bbdb-e8b07ec82201","year":2009},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:06083a81c0d23404c07aaf05ed7e66e0bc0dcd812a237afab9d7963688a75976","observation_id":"04d07009-ee30-4487-8c44-dcbb129fe363","resolution":{"observed_at":"2026-05-24T19:56:22.198947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Bayes op- timal multilabel classification via probabilistic classifier chains","venue":null,"work_id":"418ba0a0-8086-4746-9908-c275e194637d","year":2010},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:de88bcf6b7cb9fe31556956bbe1bef8895c38d49ff08bd41ae52c4dc80b43a38","observation_id":"6dba3248-c549-489d-a51b-67fefcbb47db","resolution":{"observed_at":"2026-05-24T19:56:22.168802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"On label dependence and loss minimization in multi-label classification","venue":null,"work_id":"da9dea72-79ff-424e-a78d-030f4fefa42e","year":2012},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:6cff177dba4230184276ade373474f24459d052bbdaa308589afc3e37248f87a","observation_id":"31b708bd-09e0-41e1-8d73-ba1afeaa2de5","resolution":{"observed_at":"2026-05-24T19:56:22.197292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"An analysis of chaining in multi-label classification","venue":null,"work_id":"8fcfb1ac-7067-4e89-897f-f953c1bf2504","year":2012},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:e5b1dec7194587814ea5608c68e7bd60ab0894c582fea2b8d3d4b9d83b84197f","observation_id":"ea05958e-29c7-4156-9231-b160af9f6ca0","resolution":{"observed_at":"2026-05-24T19:56:22.139753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5127dda3-ccb8-4115-927d-1940f5dffb6e","year":2003},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:d0b36929afbf3c7f30d77c5f557c303751c28645529197ac66555d25046d2289","observation_id":"98cb4137-5918-4a04-baab-492f4940be60","resolution":{"observed_at":"2026-05-24T19:56:22.166907Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Elvira, L","venue":null,"work_id":"988390a1-1910-4f1e-a644-34d1e609c9f5","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:889fd7a936f1ee7841c25202970184f6fea7c69862afed305571b5ff4b2f0ec1","observation_id":"6b695238-f653-4ae3-b564-9ae2d7d2d824","resolution":{"observed_at":"2026-05-24T19:56:22.209688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi-label classification using conditional dependency networks","venue":null,"work_id":"be8191c5-bbbf-4783-bfe5-e358d7f70c30","year":2011},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:d792090fb5bfd051404d91722bbf13b052fe9d2504116f506110df2b4d3e4404","observation_id":"9ba0b09c-8432-448f-a389-e00b49491c9b","resolution":{"observed_at":"2026-05-24T19:56:22.155447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A novel boosted-neural network ensemble for modeling multi-target regression problems","venue":null,"work_id":"4c8f5303-8853-4dd8-b3fa-cdbfe9c998f3","year":2015},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:358d076be9b66a9022ea8c59da75b301e506066488a0d31abdf24a524f29d1ab","observation_id":"0087a429-0746-4bb1-9e8c-ec9b81d9e62d","resolution":{"observed_at":"2026-05-24T19:56:22.222518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"The Elements of Statistical Learning","venue":null,"work_id":"8608bcfe-88fd-4014-bf2e-57a8aff4942c","year":2001},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:4948cf928297de35e8ed59c79c691a5fe34926d13164219d3ca8f61058e86b36","observation_id":"1e23217e-1fb3-4599-a636-fe65ac360e64","resolution":{"observed_at":"2026-05-24T19:56:22.130446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"cde9955e-8a56-4d66-9ae1-e367781b4908","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:392c083a0942a96dca87d6d6258448faa94e6f5db5b6c37a468f50549ed6936e","observation_id":"8780fe23-2bd2-489d-8185-fefe64408ff7","resolution":{"observed_at":"2026-05-24T19:56:22.188760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"09eb69c2-2873-4181-9bdb-7d4fbbb1b27e","year":2018},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:6913fa1f0c887effdc880dc639ccf6269e723dd5b16726418949e4af82dc5058","observation_id":"98512a61-92a7-4b8f-9688-5154adbbd5ab","resolution":{"observed_at":"2026-05-24T19:56:22.218918Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Conditional entropy based classifier chains for multi-label classification","venue":null,"work_id":"2a42dc5d-fd78-4405-8e3f-a36321671acd","year":2019},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:3031abb5e23eb220f1451f65b1246842057c8bde1605816235f910846a964696","observation_id":"f324a565-e428-4ed9-b556-25b3ce93d8a3","resolution":{"observed_at":"2026-05-24T19:56:22.140072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.08450","last_updated":"2020-09-24T15:10:03Z","snapshot_observed_at":"2026-08-18T18:57:12.225081Z","submitted_at":"2018-03-22T16:46:39Z","title":"A Comprehensive Analysis of Deep Regression","version":3},"cited_work":{"arxiv_id":"1803.08450","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1803.08450","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A comprehensive analysis of deep regression","venue":null,"work_id":"d7c66fec-9450-4ff8-8818-2f8432f26883","year":2018},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"cited_paper":"/paper/1803.08450","citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:bf9440ff2f7adff7af58f70a934c5e8f1d10e2a762eef0fd73bc1549cf33945c","observation_id":"6c8c0d9f-051d-43c8-92bf-6c1f16bb33df","resolution":{"observed_at":"2026-05-24T19:44:53.411497Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian Warped Gaussian Processes","venue":null,"work_id":"ccba9fda-732b-4b75-a839-fe0355bde88b","year":2012},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:1d0ba45f6eaf34fa71c3fd60c2886280444a408dc649be48c1f16be0ebac77d1","observation_id":"14cb7b65-35e6-452e-a8a4-513a46f8865a","resolution":{"observed_at":"2026-05-24T19:56:22.200639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Martino, V","venue":null,"work_id":"1c73c733-eef1-4f15-8d28-24915c607a6b","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:df9306c0d5ead97a35431bb62881777325caf8896fcaeb459801bdee53f1016a","observation_id":"09574941-756f-41b3-a03e-d6992d121402","resolution":{"observed_at":"2026-05-24T19:56:22.120654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Group importance sampling for particle filtering and MCMC","venue":null,"work_id":"8ecf29a3-b9e2-4ad4-850b-780e9df0bcd4","year":2018},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:33e6d74ce1c5a87c39e0e29aa184335ba8a13f1d1cd27b59d3cd1de8dacbfa77","observation_id":"8231a987-f318-4d18-999a-9009db8181b9","resolution":{"observed_at":"2026-05-24T19:56:22.213198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Cooper- ative parallel particle filters for online model selection and applications to urban mobility","venue":null,"work_id":"1dd5222f-544b-42d5-9543-579cd3150fb1","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:6bc412319cb40fce4b54d967087951b31bb7ea5e7e2d44c9a62439b436f1b0d7","observation_id":"0d6e53c8-42d7-4009-afa0-e0609c803818","resolution":{"observed_at":"2026-05-24T19:56:22.115013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Using A* for inference in probabilistic classifier chains","venue":null,"work_id":"430d16c5-b166-47c1-9cce-76e4ce4be024","year":2015},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:627e71db47b3abfd03344c4325a5aaa536d048127746865bed09182e331ff76a","observation_id":"bac8351f-825b-498e-be7b-c941f6d541c4","resolution":{"observed_at":"2026-05-24T19:56:22.195847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Scikit- MultiFlow: A multi-output streaming framework","venue":null,"work_id":"5bf1c545-95b0-4daa-afca-6ee87f84022a","year":2018},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:4e61970b9a0b853e69eb14960382a1d0b3dc9da79368f098b845bcf6d2846ffa","observation_id":"29ab7471-18e8-49b2-b334-587457139d02","resolution":{"observed_at":"2026-05-24T19:56:22.202280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Maximizing subset accuracy with recurrent neural networks in multi-label classification","venue":null,"work_id":"15138752-77f8-4f96-9a46-9679a28e7e70","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:a03685dee9c7b28719ab5d1eddffb59d51c8d9f35fd26c58788b2427cb0eb856","observation_id":"58a2d40c-8c54-4c05-901e-1b9b6db78331","resolution":{"observed_at":"2026-05-24T19:56:22.180385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Qui˜ nonero-Candela, A","venue":null,"work_id":"4e08df96-8db2-4e92-932a-c8e363ab7850","year":2003},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:a51bfae501ab053545accb0e85c96c945dc344201dcd4bf95a19f0cce201036e","observation_id":"9a707238-9e08-42c9-805a-032bb95d19e3","resolution":{"observed_at":"2026-05-24T19:56:22.204550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi-dimensional clas- sification with super-classes","venue":null,"work_id":"ff6f7aa3-2460-4955-8253-ff0cc4816591","year":2014},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:54ea7f8870a40501c4d8d04363bb2aea903534e4be1606dc8e3f4f46baf07723","observation_id":"cc62930f-bdf1-401f-88cf-4bfea9282f1e","resolution":{"observed_at":"2026-05-24T19:56:22.216408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.09022","last_updated":"2017-07-18T12:12:49Z","snapshot_observed_at":"2026-08-14T22:54:14.251401Z","submitted_at":"2015-03-31T12:29:13Z","title":"Multi-label Classification using Labels as Hidden Nodes","version":3},"cited_work":{"arxiv_id":"1503.09022","doi":null,"metadata_source":"pith","pith_arxiv_id":"1503.09022","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-label Classification using Labels as Hidden Nodes","venue":"stat.ML","work_id":"a8722365-7460-4dcc-9a8a-61ef822fe49c","year":2015},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"cited_paper":"/paper/1503.09022","citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:40e681846c0d8becdbbde72628474426568a57c984d81895401946dcbeca5a2c","observation_id":"10ffb6dc-06b0-407e-929c-97c6bdc2ea8d","resolution":{"observed_at":"2026-05-24T19:44:53.432963Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi-label methods for prediction with sequential data","venue":null,"work_id":"db265bf2-d0e6-4de8-b1b3-26d7e044c010","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:42d532457b76f4c50a4d367a6479a9444ea4e93f5f1840e40c0b7849fb804042","observation_id":"8dfbfbc0-7e76-47cb-a099-9b0802b57650","resolution":{"observed_at":"2026-05-24T19:56:22.171714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi-label methods for prediction with sequential data","venue":null,"work_id":"faa40eb2-a33a-43cd-805e-059f40885795","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:e09c877c11b43edb04df63687ea95a5ea8076c2502f8c01ae72aaec887a31aeb","observation_id":"b8773799-6524-4362-a00d-69c0aa38c9b9","resolution":{"observed_at":"2026-05-24T19:56:22.212275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Efficient Monte Carlo meth- ods for multi-dimensional learning with classifier chains","venue":null,"work_id":"a638d390-f51c-4ed7-8141-a7be03eacf1e","year":2014},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:fca0912eaa2d37a668a93ca25909b5a483ceca68f76ee32df05645ce5451458e","observation_id":"0608522f-632d-4a39-93be-cd23a9664e4b","resolution":{"observed_at":"2026-05-24T19:56:22.206538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Olmos, and David Luengo","venue":null,"work_id":"2f40de09-8579-4e3d-9d47-4252c93fb55e","year":2096},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:2424702431a04c7b3647de010977d83aa1c718cb35930a055ae2cce7dfd6a430","observation_id":"3375f98e-1b36-4d0f-8ce3-e3e4168e881d","resolution":{"observed_at":"2026-05-24T19:56:22.143062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Classi- fier chains for multi-label classification","venue":null,"work_id":"3e9dc357-615c-446a-b008-a6314c655817","year":2011},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:9a7ced7fc81105bbae0388d544a4207244b47a163f4790d469ab7b0859fd2bc5","observation_id":"6d9703ab-cf66-4ad2-a74c-05c24df32c82","resolution":{"observed_at":"2026-05-24T19:56:22.147097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Snelson, Z","venue":null,"work_id":"77def16d-6676-4571-95df-9023b9dfff30","year":2003},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:2de930e8143c98b0fddc331cd88980346fd8d33d8b3027b80316d82d86aba8d7","observation_id":"684a4716-dc75-4d84-8361-d7d07a17e253","resolution":{"observed_at":"2026-05-24T19:56:22.225959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi-target regression via input space expansion: treating targets as inputs","venue":null,"work_id":"683dbbba-769b-4d36-97f4-cff0511e3680","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:cf5b9518d24e4adb0e963779aa3e5ec47bb915f1602585310f9a4e42280703a6","observation_id":"be7adc79-fee0-4d04-b43f-f881820c12a1","resolution":{"observed_at":"2026-05-24T19:56:22.114227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Ccnet: Joint multi-label classification and feature selec- tion using classifier chains and elastic net regularization","venue":null,"work_id":"daeab001-8d61-4548-a379-6454497e3612","year":2017},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:6eb41e7b707f6c326b4ffd6000e33d12889e753abb72376bc4a4cad33f9233a9","observation_id":"db5399f5-4c5b-4fc1-9fb6-99f78fa2473b","resolution":{"observed_at":"2026-05-24T19:56:22.216170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Random k- labelsets for multi-label classification","venue":null,"work_id":"319351c9-0de0-443a-ac6a-02712949967c","year":2011},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:df7c8d788b58ff87dd54359af89c10302b53168e2d268dd295072325b7722d53","observation_id":"7d964f30-e096-4844-9e44-6da71ec208e2","resolution":{"observed_at":"2026-05-24T19:56:22.208969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Multi- target prediction: A unifying view on problems and methods","venue":null,"work_id":"def48644-a966-4bf4-b3e3-350a8dd24072","year":2018},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:690f08376270478ae6385939053291e6f525cd5fb479ff754aaf6e419100e817","observation_id":"475c7033-7188-4e24-9c01-4b7a8db4d77b","resolution":{"observed_at":"2026-05-24T19:56:22.192351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Monte carlo tree search in continuous action spaces with execution uncertainty","venue":null,"work_id":"f61ea94c-486c-497c-8cd4-7472c4186f26","year":2016},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:2fc7913bfb31cbbb6f73d1719fdebaaa96ee86c1bdeb09c420ca30120d971e8d","observation_id":"3663421d-a255-4ad1-95c6-caef938fb78b","resolution":{"observed_at":"2026-05-24T19:56:22.229576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"44b5cbb4-e59b-45de-9b10-59ee1be4319c","year":null},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:07f5189b87d160a1365df31bf1ea79edbf35d76895cdc92d0f43a35d4ce7615b","observation_id":"5901d760-afc4-4c3f-b128-0b44627156c5","resolution":{"observed_at":"2026-05-24T19:56:22.193018Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2a4fea8b-35e3-4845-be74-eaed1a2ba25a","year":null},"citing_paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-24T19:42:43.916120Z"},"links":{"citing_paper":"/paper/1907.08087"},"observation_digest":"sha256:be61e0391d2b9ba8717f015a9d691648019095aa5b0fbd92242633421ce05a72","observation_id":"3ef404f0-3e2d-465f-9499-0ef201c39145","resolution":{"observed_at":"2026-05-24T19:56:22.188361Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1907.08087","last_updated":"2019-07-18T14:41:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:08:38.481574Z","submitted_at":"2019-07-18T14:41:25Z","title":"Probabilistic Regressor Chains with Monte Carlo Methods"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":4,"verified_exact":3,"verified_fuzzy":36},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1907.08087."}