Paper Citation Record · LEDGER

Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.04105.

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

pith.paper-citation-record.v1
2504.04105 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:29:38.789567Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:59:39.677560Z

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 54ee697a-fe65-4c67-b691-bdcd1ecc9406 · inbound

Efficient Logistic Regression with Mixture of Sigmoids cites this paper.

Efficient Logistic Regression with Mixture of Sigmoids Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:48:15.645533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b85f79f-5d6c-45c7-8f4a-dfb01947cb51 · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.666952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0cd64b23-46ab-4cc5-9a2c-20647501fa09 · inbound

Large-Step Training Dynamics of a Two-Factor Linear Transformer Model cites this paper.

Large-Step Training Dynamics of a Two-Factor Linear Transformer Model Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:53:56.335715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b80b2a7-9040-491e-8f29-1510d6648c73 · inbound

Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design cites this paper.

Finite-Sample Performance of Gradient Descent in Logistic Regression with Gaussian Design Minimax Optimal Convergence of Gradient Descent in Logistic Regression via Large and Adaptive Stepsizes

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:59:39.678851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.