{"as_of":"2026-08-17T00:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bdb7a13843da41e38caed132aca0c8e9fdf19dee1509bc30bca205d6da1c4d37","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T22:46:55.629970Z","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":"2026-05-11T20:41:09.391568Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.05830","last_updated":"2020-02-21T17:08:10Z","snapshot_observed_at":"2026-07-06T08:21:19.915142Z","submitted_at":"2019-09-12T17:37:08Z","title":"Differentially Private Meta-Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.05830","snapshot_observed_at":"2026-08-15T22:46:55.629970Z","title":"Differentially private meta- learning","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2505.06651","last_updated":"2025-05-10T13:57:57Z","snapshot_observed_at":"2026-08-15T22:34:39.041126Z","submitted_at":"2025-05-10T13:57:57Z","title":"Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T22:46:55.629970Z"},"links":{"cited_paper":"/paper/1909.05830","citing_paper":"/paper/2505.06651"},"observation_digest":"sha256:0966a1be940a5f134f8940f97a2422a81c98c2402d37a98b81bb3c6649ef6990","observation_id":"7f02f943-1e0b-4e99-a836-f5401a0dfdd4","resolution":{"observed_at":"2026-08-15T22:46:55.629970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05830","last_updated":"2020-02-21T17:08:10Z","snapshot_observed_at":"2026-07-06T08:21:19.915142Z","submitted_at":"2019-09-12T17:37:08Z","title":"Differentially Private Meta-Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.05830","snapshot_observed_at":"2026-08-04T18:23:50.351075Z","title":", author Khodak, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10041","last_updated":"2025-09-12T08:08:48Z","snapshot_observed_at":"2026-08-13T00:46:39.718587Z","submitted_at":"2025-09-12T08:08:48Z","title":"FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-04T18:23:50.351075Z"},"links":{"cited_paper":"/paper/1909.05830","citing_paper":"/paper/2509.10041"},"observation_digest":"sha256:cc19404b1ddc1ece0b248e6deea94fe218932fa12a8953dadaae67cb726b7958","observation_id":"7b48b2d0-f1fa-47b8-8046-e67c62d1a5b2","resolution":{"observed_at":"2026-08-04T18:23:50.351075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05830","last_updated":"2020-02-21T17:08:10Z","snapshot_observed_at":"2026-07-06T08:21:19.915142Z","submitted_at":"2019-09-12T17:37:08Z","title":"Differentially Private Meta-Learning","version":2},"cited_work":{"arxiv_id":"1909.05830","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.05830","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4e2cd92c-eaaf-43bd-9821-97623def3b59","year":2019},"citing_paper":{"arxiv_id":"2604.23426","last_updated":"2026-04-25T19:43:05Z","snapshot_observed_at":"2026-08-14T10:02:02.097946Z","submitted_at":"2026-04-25T19:43:05Z","title":"Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T08:24:14.745888Z"},"links":{"cited_paper":"/paper/1909.05830","citing_paper":"/paper/2604.23426"},"observation_digest":"sha256:f3b16cbe58019cd8245a8639196233257cac892f4ff23a2175414336b94e9ef4","observation_id":"bf975ed7-9497-42b2-b354-5b73bdb88aac","resolution":{"observed_at":"2026-05-11T20:41:09.395630Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05830","last_updated":"2020-02-21T17:08:10Z","snapshot_observed_at":"2026-07-06T08:21:19.915142Z","submitted_at":"2019-09-12T17:37:08Z","title":"Differentially Private Meta-Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.05830","snapshot_observed_at":"2026-07-14T04:29:35.143178Z","title":"Differentially private meta-learning.arXiv preprint arXiv:1909.05830, 2019","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2607.11600","last_updated":"2026-07-13T14:25:04Z","snapshot_observed_at":"2026-08-15T00:23:57.447641Z","submitted_at":"2026-07-13T14:25:04Z","title":"Privacy-Aware Collaborative and Distributed Bayesian Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T04:29:35.143178Z"},"links":{"cited_paper":"/paper/1909.05830","citing_paper":"/paper/2607.11600"},"observation_digest":"sha256:6dfe4d7dab59dce7a02d68d5ee5870a121f361cd3bd60b5ccca90259c0f65165","observation_id":"18bfa907-04ad-4bbb-8f6e-be24d8c99eea","resolution":{"observed_at":"2026-07-14T04:29:35.143178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1909.05830/citation-record","integrity":"/paper/1909.05830/integrity","json":"/paper/1909.05830/citation-record.json","paper":"/paper/1909.05830"},"outbound":[],"paper":{"arxiv_id":"1909.05830","last_updated":"2020-02-21T17:08:10Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:21:19.915142Z","submitted_at":"2019-09-12T17:37:08Z","title":"Differentially Private Meta-Learning"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1909.05830."}