Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.06915.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:39:53.879939Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T19:28:52.528524Z
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 3d5632ef-db73-40ce-b49e-cbe84a42e6c4 · inbound
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling cites this paper.
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Reference 2008
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:39:53.879939Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03b101fb-9a0e-4b2c-ba04-15fefe580962 · inbound
Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization cites this paper.
Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T10:46:25.335756Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d144c7f-b68b-422c-8bfd-4731c5e46feb · inbound
When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold cites this paper.
When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:31:14.275217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7fe65622-8493-4bf8-a2fc-252cda424243 · inbound
Factor Augmented High-Dimensional SGD cites this paper.
Factor Augmented High-Dimensional SGD Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Reference 89
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T03:33:01.629578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 64717453-e200-4f56-8451-67a5ed1c60e5 · inbound
A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning cites this paper.
A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T19:28:52.530044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.