Paper Citation Record · LEDGER

On the Convergence of SGD with Biased Gradients

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2008.00051.

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

pith.paper-citation-record.v1
2008.00051 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:35.283217Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

16
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 73ecdd2d-78b4-4ff4-8022-25482c219537 · inbound

Verification of Machine Unlearning is Fragile cites this paper.

Verification of Machine Unlearning is Fragile On the Convergence of SGD with Biased Gradients

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:25:50.536454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e0e1739-bfa8-4629-b538-6d058bbd41d2 · inbound

Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization cites this paper.

Stacey: Promoting Stochastic Steepest Descent via Accelerated $\ell_p$-Smooth Nonconvex Optimization On the Convergence of SGD with Biased Gradients

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:35.283217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfd23108-f78d-4794-86c6-2cd50b394b1b · inbound

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees cites this paper.

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees On the Convergence of SGD with Biased Gradients

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:20:07.917254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67f6aa36-8aab-41fc-bc17-6767fa0759bb · inbound

Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks cites this paper.

Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks On the Convergence of SGD with Biased Gradients

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T10:19:08.874670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 711c965d-bef1-4183-9ddc-9ba52b79653f · inbound

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning cites this paper.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning On the Convergence of SGD with Biased Gradients

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T11:48:10.570043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25cf8e08-6015-46cf-89ba-dcf3c5b27c71 · inbound

Discrete State Diffusion Models: A Sample Complexity Perspective cites this paper.

Discrete State Diffusion Models: A Sample Complexity Perspective On the Convergence of SGD with Biased Gradients

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T10:22:58.326295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b4c3a94-5ba3-4c21-ac2e-9e7efd16f57b · inbound

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models cites this paper.

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models On the Convergence of SGD with Biased Gradients

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:15:22.255624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f5cf886-a636-4217-bebb-29844a380db0 · inbound

On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization On the Convergence of SGD with Biased Gradients

Reference 154

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:36:08.560133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ceee93d-0ad8-47b1-abff-523ec59e9f39 · inbound

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity cites this paper.

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity On the Convergence of SGD with Biased Gradients

Reference 217

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:32:52.147043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 796735df-e8c1-493c-b227-b00dfd44dc5d · inbound

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing cites this paper.

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing On the Convergence of SGD with Biased Gradients

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:18:59.256024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a58217d0-0695-421a-92ab-0f69610fc47d · inbound

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models On the Convergence of SGD with Biased Gradients

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:08:54.375386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 843f7646-f85f-4356-8961-45b019d135fe · inbound

Stochastic Optimization and Data Science cites this paper.

Stochastic Optimization and Data Science On the Convergence of SGD with Biased Gradients

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T21:12:47.227680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5a8bdf2-af6e-408a-a38e-6fd17556d123 · inbound

Stochastic Optimization and Data Science cites this paper.

Stochastic Optimization and Data Science On the Convergence of SGD with Biased Gradients

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T16:40:49.519929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f28ef943-bea4-46e6-9944-8d42086d75aa · inbound

Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs cites this paper.

Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs On the Convergence of SGD with Biased Gradients

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:29:02.055139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abf2c709-16ec-4e16-af25-96b0a78bf770 · inbound

Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design cites this paper.

Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design On the Convergence of SGD with Biased Gradients

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T16:04:53.055841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2630799a-6d2d-4d12-833c-6656ad805507 · inbound

A Gradient Flow Perspective on Minimum MMD Estimation cites this paper.

A Gradient Flow Perspective on Minimum MMD Estimation On the Convergence of SGD with Biased Gradients

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-11T23:22:05.432957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.