Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.20088.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:40:42.774009Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T08:03:08.712421Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation b96014c7-e5c1-4303-b48d-7fc28f6d6a0d · inbound
Sufficiency-principled Transfer Learning via Model Averaging cites this paper.
Sufficiency-principled Transfer Learning via Model Averaging Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:40:42.774009Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 21611a9a-87e5-4caf-beac-9fe997fabbb1 · inbound
Optimal differentially private kernel learning with random projection cites this paper.
Optimal differentially private kernel learning with random projection Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T03:12:00.779685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b4877892-56be-4cee-9eb0-dcd14ad895f7 · inbound
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning cites this paper.
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T08:03:08.715972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 269b3aa3-41da-435b-b68f-00e4d7d1a097 · inbound
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning cites this paper.
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T07:18:06.911807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6866608a-95f3-4c4b-803b-625e39a0b540 · inbound
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning cites this paper.
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T16:29:28.243675Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.