Paper Citation Record · LEDGER

Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.11573.

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

pith.paper-citation-record.v1
2006.11573 v1

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 2026-08-15T20:44:56.974655Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T05:49:40.666173Z

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 3697669c-3a0a-432f-930b-8afefdbc7bc6 · inbound

The Stochastic Multi-Proximal Method for Nonsmooth Optimization cites this paper.

The Stochastic Multi-Proximal Method for Nonsmooth Optimization Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:44:56.974655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ef6fcf4-e5c2-46c0-a1f3-c34e70be20eb · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 117

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.258858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bbc596e-1967-4574-979b-174dccca3f5a · inbound

Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits cites this paper.

Scalable Distributed Stochastic Optimization via Bidirectional Compression: Beyond Pessimistic Limits Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:05:54.877488Z

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.

Observation 29a387d1-2f0a-4292-b072-c764c482a0bd · inbound

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction cites this paper.

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 178

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:51:33.407134Z

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.

Observation 3d83436d-b106-4342-99e2-8ac33184266a · inbound

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method cites this paper.

Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 180

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.557081Z

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.

Observation 79f00ebc-8426-4860-adab-c99f23c14723 · inbound

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging cites this paper.

LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization

Reference 181

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:49:40.667550Z

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.