{"as_of":"2026-08-09T09:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:50d9262219f79e1388efbff70e1f5bbde6b5284e71308c1a67db7ed8e613c713","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:42:49.217847Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T23:50:47.469669Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.03813","last_updated":"2021-04-23T23:07:28Z","snapshot_observed_at":"2026-07-06T09:36:21.791292Z","submitted_at":"2020-07-07T22:31:01Z","title":"Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03813","snapshot_observed_at":"2026-08-07T13:42:49.217847Z","title":"Bypassing the ambient dimension: Private sgd with gradient subspace identification.arXiv preprint arXiv:2007.03813, 2020","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.21382","last_updated":"2025-05-27T16:10:53Z","snapshot_observed_at":"2026-08-09T04:47:45.769227Z","submitted_at":"2025-05-27T16:10:53Z","title":"DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T13:42:49.217847Z"},"links":{"cited_paper":"/paper/2007.03813","citing_paper":"/paper/2505.21382"},"observation_digest":"sha256:0cee8c722a9fa68d206831987cd234a50b25925991667a863875421d8eb9531f","observation_id":"d16f0108-77ed-41b4-80fe-336124c61a32","resolution":{"observed_at":"2026-08-07T13:42:49.217847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03813","last_updated":"2021-04-23T23:07:28Z","snapshot_observed_at":"2026-07-06T09:36:21.791292Z","submitted_at":"2020-07-07T22:31:01Z","title":"Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification","version":2},"cited_work":{"arxiv_id":"2007.03813","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.03813","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"90f65d43-56da-48cf-9224-4255ef554490","year":2007},"citing_paper":{"arxiv_id":"2506.15588","last_updated":"2026-05-18T12:41:38Z","snapshot_observed_at":"2026-08-02T14:39:33.262823Z","submitted_at":"2025-06-18T16:05:09Z","title":"Memory-Efficient Differentially Private Training with Gradient Random Projection","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-21T23:46:54.620034Z"},"links":{"cited_paper":"/paper/2007.03813","citing_paper":"/paper/2506.15588"},"observation_digest":"sha256:4b7f07beae49cfd26c7849c76d0b92721b7bae51d405431d0872a81177d349e1","observation_id":"ad297307-2a84-4f20-b11a-3156d95643c5","resolution":{"observed_at":"2026-05-21T23:50:47.472257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03813","last_updated":"2021-04-23T23:07:28Z","snapshot_observed_at":"2026-07-06T09:36:21.791292Z","submitted_at":"2020-07-07T22:31:01Z","title":"Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03813","snapshot_observed_at":"2026-08-06T19:06:27.025962Z","title":"Bypassingtheambientdimension: Private SGD with gradient subspace identification.arXiv:2007.03813,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.06525","last_updated":"2025-07-09T03:53:03Z","snapshot_observed_at":"2026-08-08T23:11:55.425212Z","submitted_at":"2025-07-09T03:53:03Z","title":"AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:06:27.025962Z"},"links":{"cited_paper":"/paper/2007.03813","citing_paper":"/paper/2507.06525"},"observation_digest":"sha256:3b09cb7ade1b4b2b7111c7bf785c871ce515d8ef6c0d11186a596fc330f3fc95","observation_id":"ecbb342a-1348-42b7-9873-69e375add08b","resolution":{"observed_at":"2026-08-06T19:06:27.025962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.03813","last_updated":"2021-04-23T23:07:28Z","snapshot_observed_at":"2026-07-06T09:36:21.791292Z","submitted_at":"2020-07-07T22:31:01Z","title":"Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.03813","snapshot_observed_at":"2026-08-04T19:11:57.452693Z","title":"Bypassing the ambient dimension: Private sgd with gradient subspace identification","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2509.09485","last_updated":"2025-09-12T01:27:15Z","snapshot_observed_at":"2026-08-07T14:36:54.609466Z","submitted_at":"2025-09-11T14:17:04Z","title":"Balancing Utility and Privacy: Dynamically Private SGD with Random Projection","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-04T19:11:57.452693Z"},"links":{"cited_paper":"/paper/2007.03813","citing_paper":"/paper/2509.09485"},"observation_digest":"sha256:fc4abbee45df6d3bbf2e5f69f1af1e30df4a0cd45e2f10c5407050269e216a24","observation_id":"b7cea909-8d01-41d4-a7ee-57af05091880","resolution":{"observed_at":"2026-08-04T19:11:57.452693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2007.03813/citation-record","integrity":"/paper/2007.03813/integrity","json":"/paper/2007.03813/citation-record.json","paper":"/paper/2007.03813"},"outbound":[],"paper":{"arxiv_id":"2007.03813","last_updated":"2021-04-23T23:07:28Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:36:21.791292Z","submitted_at":"2020-07-07T22:31:01Z","title":"Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2007.03813."}