Paper Citation Record · LEDGER

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2508.21106.

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

pith.paper-citation-record.v1
2508.21106 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:57:46.266050Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

verified exact1
verified fuzzy14
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0cb8724-a1a0-4152-b4bd-437fe7a20bff · outbound

This paper cites A differential equation for modeling nesterov’s accelerated gradient method: Theory and insights.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models A differential equation for modeling nesterov’s accelerated gradient method: Theory and insights

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.991250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8cce668e-40ce-4891-9ead-c2b60f3246ab · outbound

This paper cites A lyapunov analysis for accelerated gradient methods: From determinis- tic to stochastic case.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models A lyapunov analysis for accelerated gradient methods: From determinis- tic to stochastic case

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.969809Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e15d7023-7857-49bb-9672-d2c6c8c4d130 · outbound

This paper cites NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer

Reference 3

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Source-reported events for the cited work

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Observation 9f4034d1-9e88-42d0-8c2b-133d0439c8ed · outbound

This paper cites A method of solving a convex programming problem with convergence rate O(1/k2).

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models A method of solving a convex programming problem with convergence rate O(1/k2)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.943868Z

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Observation 1f604b57-c3d5-4e9b-90a1-e228f8201b34 · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Adaptive subgradient methods for online learning and stochastic optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:46.185934Z

Source-reported events for the cited work

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Observation a56d38a7-ff15-4714-af8e-cb9b6337dc05 · outbound

This paper cites Kingma and Jimmy Ba.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Kingma and Jimmy Ba

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.764386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 12472c65-1927-4bad-b67e-fd882206cf75 · outbound

This paper cites Hinton, Nitish Srivastava, and Kevin Swersky.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Hinton, Nitish Srivastava, and Kevin Swersky

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.739792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 01fe4d20-3e98-423a-8786-81acc332cc3a · outbound

This paper cites Fast symmetric factorization of hierarchical matrices with applications.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Fast symmetric factorization of hierarchical matrices with applications

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:46.202967Z

Source-reported events for the cited work

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Observation 0aa6c51b-0928-4bab-9588-b5810dd92272 · outbound

This paper cites Buhmann, and Nicolai Meinshausen.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Buhmann, and Nicolai Meinshausen

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.716678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0bc8450f-d7c5-4c63-ae42-0586c1a37f3e · outbound

This paper cites Ravikumar.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Ravikumar

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.697408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a07c0f1d-7372-418c-a196-bc556810cccd · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization, 2018.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Shampoo: Preconditioned stochastic tensor optimization, 2018

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:46.218132Z

Source-reported events for the cited work

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Observation eb859d9d-e365-4b0e-89ef-708bc84307a0 · outbound

This paper cites SOAP: One-shot pruning of generative pre-trained transformer checkpoints with a second-order method, 2024.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models SOAP: One-shot pruning of generative pre-trained transformer checkpoints with a second-order method, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.658323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3025c056-192e-4382-98f7-0891e9606cc1 · outbound

This paper cites Efficient full-matrix adaptive regularization, 2020.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Efficient full-matrix adaptive regularization, 2020

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.634423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a62dd9a9-8575-4258-ae4f-a4ee28a7daf8 · outbound

This paper cites Remove that square root: A new efficient scale-invariant version of adagrad.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Remove that square root: A new efficient scale-invariant version of adagrad

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.611913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 36ca58d6-a7d4-471d-b8d9-2919ab651a07 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models ADADELTA: An Adaptive Learning Rate Method

Reference 15

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Source-reported events for the cited work

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Observation c7b2bf66-7b46-4633-8ead-40e160ba2c09 · outbound

This paper cites Incorporating nesterov momentum into adam.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Incorporating nesterov momentum into adam

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.590831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7352e02f-67d1-41a5-894f-808dec55c27f · outbound

This paper cites Fast low-rank modifications of the thin singular value decomposition.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Fast low-rank modifications of the thin singular value decomposition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.570242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b37ed6e1-23f7-4cb2-900c-420b9a0f688d · outbound

This paper cites A projector-splitting integrator for dynamical low-rank approximation, 2013.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models A projector-splitting integrator for dynamical low-rank approximation, 2013

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.551178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3b54426-bfbb-4add-a11f-344871e35d59 · outbound

This paper cites an unresolved cited work.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Unresolved cited work

Reference 19

Resolution: verified exact; raw_fallback, observed 2026-08-05T14:57:46.421800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2a5993ae-62b6-4620-b3c5-93b0416ca442 · outbound

This paper cites UCI machine learning repository, 2017.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models UCI machine learning repository, 2017

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:46.261072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f496f1d-2f37-423d-b3fa-d1d2bb559be0 · outbound

This paper cites Stress test procedure for feature selection algorithms.

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models Stress test procedure for feature selection algorithms

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:57:46.515857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.