Paper Citation Record · LEDGER

Almost Tune-Free Variance Reduction

As of 16 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:1908.09345.

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

pith.paper-citation-record.v1
1908.09345 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:20:21.702645Z

measured 13 of 13 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 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

13 of 13 outbound references displayed

verified exact3
verified fuzzy7
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68f49808-3ef5-4966-b610-6adf2a5f6858 · outbound

This paper cites First, notice that 2ηL/(1 +κ)>µη =δ, which implies that 1−δ >1− [2ηL/(1 +κ)].

Almost Tune-Free Variance Reduction First, notice that 2ηL/(1 +κ)>µη =δ, which implies that 1−δ >1− [2ηL/(1 +κ)]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.870955Z

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 42d4c875-7515-4da9-9d71-41a605698ab3 · outbound

This paper cites Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop.

Almost Tune-Free Variance Reduction Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T11:20:21.657951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85a33c75-6bb3-4b5b-92ea-5a49c862aa6c · outbound

This paper cites Proof of Theorem.

Almost Tune-Free Variance Reduction Proof of Theorem

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.884844Z

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 693932a1-77c2-4c01-abae-bbc7e03630d9 · outbound

This paper cites In order to prove Theorem 2, we need to borrow the following result from [Nguyen et al., 2017].

Almost Tune-Free Variance Reduction In order to prove Theorem 2, we need to borrow the following result from [Nguyen et al., 2017]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.898645Z

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 03a32f0e-8764-40a5-99ec-48d22d22d054 · outbound

This paper cites Sinceηs inﬂuences convergence, we will useλs to denote the convergence rate of the inner loops, that is, E[f(˜xs)−f(x∗)]≤λsE[f(˜xs−1)−f(x∗)].

Almost Tune-Free Variance Reduction Sinceηs inﬂuences convergence, we will useλs to denote the convergence rate of the inner loops, that is, E[f(˜xs)−f(x∗)]≤λsE[f(˜xs−1)−f(x∗)]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.856422Z

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 f44f3725-b6b9-4e9a-83b3-189a9181a410 · outbound

This paper cites After a simple derivation, one can have the convergence rate λs = 2ηsL 2−ηsL + 2(1 +ηsL) ( 1− 2ηsL 1 +κ )m.

Almost Tune-Free Variance Reduction After a simple derivation, one can have the convergence rate λs = 2ηsL 2−ηsL + 2(1 +ηsL) ( 1− 2ηsL 1 +κ )m

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.842241Z

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 eeca0a32-9dab-4a93-9c43-d9550030fc8e · outbound

This paper cites Optimization Methods for Large-Scale Machine Learning.

Almost Tune-Free Variance Reduction Optimization Methods for Large-Scale Machine Learning

Reference 1988

Resolution: unresolved; no resolver link, observed 2026-08-14T11:20:21.642556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ee068a-3b1b-4fc5-a619-152229dc187c · outbound

This paper cites Semi-Stochastic Gradient Descent Methods.

Almost Tune-Free Variance Reduction Semi-Stochastic Gradient Descent Methods

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-14T11:20:21.653045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8c6509b-e468-40aa-b012-3c0043642173 · outbound

This paper cites Accelerating Mini-batch SARAH by Step Size Rules.

Almost Tune-Free Variance Reduction Accelerating Mini-batch SARAH by Step Size Rules

Reference 2014

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:20:21.743819Z

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 71c36195-9f64-44d0-a9fe-fe9dc5bb4d21 · outbound

This paper cites A class of stochastic variance reduced methods with an adaptive stepsize.

Almost Tune-Free Variance Reduction A class of stochastic variance reduced methods with an adaptive stepsize

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.929113Z

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 d663f5b2-197d-4e93-839d-04d297d58173 · outbound

This paper cites A proximal stochastic gradient method with progressive variance reduction.

Almost Tune-Free Variance Reduction A proximal stochastic gradient method with progressive variance reduction

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:20:21.913754Z

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 ff47c4cf-daad-4106-991a-ce1b40e7d35a · outbound

This paper cites Adaptive Step Sizes in Variance Reduction via Regularization.

Almost Tune-Free Variance Reduction Adaptive Step Sizes in Variance Reduction via Regularization

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:20:21.764470Z

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 d821f396-52d0-4b83-8fe0-03337cee2555 · outbound

This paper cites Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs.

Almost Tune-Free Variance Reduction Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:20:21.812942Z

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.

Pith citing papers

No inbound Pith citation observations are available.