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Paper Citation Record · LEDGER

Learning subgaussian classes : Upper and minimax bounds

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1305.4825.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1305.4825 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:36.712015Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-09T02:35:53.832702Z

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 9ddd14e1-399b-4f77-8207-658a5b34b92e · inbound

On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors cites this paper.

On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors Learning subgaussian classes : Upper and minimax bounds

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T05:12:14.231175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T05:12:14.231175Z digest=sha256:649067d4fa02d75235a207e98ec7a0114638a8bb64ec4e8a1e3b279939071dde

Observation d8be3dec-c41c-45a8-a741-4fd6b1f89bf1 · inbound

On the Efficiency of ERM in Feature Learning cites this paper.

On the Efficiency of ERM in Feature Learning Learning subgaussian classes : Upper and minimax bounds

Reference 2012

Resolution
unresolved
no resolver link, observed 2026-08-12T18:06:35.969380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:06:35.969380Z digest=sha256:1f82fb8bc245a690b94aa81b3088911a014702d91bf3d3d88390afd9b32a54c7

Observation 4dce5a64-e32c-436e-b8e0-01b49296c123 · inbound

Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs cites this paper.

Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs Learning subgaussian classes : Upper and minimax bounds

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T22:54:24.924833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:54:24.924833Z digest=sha256:4396d5675cd539533ca5568b85a228d927a84c31e2393b06fb3950e5e9eac958

Observation 85f2a205-f404-4abe-b49f-e97c7cff4591 · inbound

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models cites this paper.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Learning subgaussian classes : Upper and minimax bounds

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T16:44:36.712015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:44:36.712015Z digest=sha256:f7d5a1472b8f576f51116df61efe9e01383c516c738d19a97a395511f7262d70

Observation e6fc3f04-8dd1-4dfd-99fe-6496acddfc2f · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Learning subgaussian classes : Upper and minimax bounds

Reference 212

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T11:56:56.035264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-28T02:35:39.845487Z digest=sha256:0dd5e9e608e2c52c6e9a95cc00f680f39e9d8f6cdd5a51e37d7fd52d76d163b5

Observation 42af68fe-1409-4c08-9e13-65a3f0992ed4 · inbound

Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity cites this paper.

Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity Learning subgaussian classes : Upper and minimax bounds

Reference 270

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T02:35:53.833909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-07-09T02:31:10.557742Z digest=sha256:16481a7841fff4f5a94c484ca663416c02751ba41c084967e043203017c38a15