Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-23T03:29:27.821389Z
Paper Citation Record · LEDGER
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
As of 4 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 6 inbound Pith citation observations for arXiv:2502.06719.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T16:26:55.468917Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-01T21:46:15.184522Z
Reference resolution
63 of 63 outbound references displayed
- verified exact7
- verified fuzzy41
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f766090b-5305-4c7f-86cf-493b04a542d5 · outbound
This paper cites High-dimensional central limit theorems for linear functionals of online least-squares sgd.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent High-dimensional central limit theorems for linear functionals of online least-squares sgd
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7797aba1-9ddf-4001-9587-a948bc59b115 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation a1e739ec-f4b7-4eaa-a659-2be5fe69d31d · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 3
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Observation 446817d9-5eee-4cf3-8bc4-c62d5bd2fa31 · outbound
This paper cites The reverse isoperimetric problem for Gaussian measure.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent The reverse isoperimetric problem for Gaussian measure
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 8fcc5e63-0bc2-484a-8395-5cf0c010f797 · outbound
This paper cites Barsov and V.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Barsov and V
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation ca40b6e0-137d-4c56-bff0-853eade55d5c · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8e5f729e-d204-45a5-96a9-2cb314c93473 · outbound
This paper cites Benveniste, M.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Benveniste, M
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 58fe3626-dd57-4c24-80cf-9a9423521c66 · outbound
This paper cites Convex optimization: Algorithms and complexity.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Convex optimization: Algorithms and complexity
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 19d50b1f-7b37-48b7-97bf-9f6a03f03b7d · outbound
This paper cites Statistical inference for online decision making via stochastic gradient descent.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Statistical inference for online decision making via stochastic gradient descent
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation 3df79c82-a7d6-4144-9199-f04ca72e259e · outbound
This paper cites Online statistical inference for contextual bandits via stochastic gradient descent.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Online statistical inference for contextual bandits via stochastic gradient descent
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3f361204-0815-4f82-b156-728221358014 · outbound
This paper cites Lee, Xin T.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Lee, Xin T
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2bfdbad3-1c13-43c0-b045-58e7eaa8475d · outbound
This paper cites SAGA: A fast incremental gradi- ent method with support for non-strongly convex composite objectives.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent SAGA: A fast incremental gradi- ent method with support for non-strongly convex composite objectives
Reference 12
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1258802a-0f8e-4b9d-91b5-70a7f8809364 · outbound
This paper cites The total variation distance between high-dimensional Gaussians with the same mean.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent The total variation distance between high-dimensional Gaussians with the same mean
Reference 13
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c0d3b2b9-9fa1-4fa1-bf0e-145e1deb2a8d · outbound
This paper cites Bridging the gap between constant step size stochastic gradient descent and Markov chains.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Bridging the gap between constant step size stochastic gradient descent and Markov chains
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation cdd6d5ed-9cc7-4e6a-a0f1-c0cd625e210a · outbound
This paper cites Bootstrap methods: another look at the jackknife.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Bootstrap methods: another look at the jackknife
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 814a6680-bb28-435c-8ac7-217d30633b08 · outbound
This paper cites Online bootstrap confidence intervals for the stochastic gradient descent estimator.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Online bootstrap confidence intervals for the stochastic gradient descent estimator
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ee3950bd-9400-4c2b-9b89-0cef391258c4 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation e918dcc2-fbbf-475f-9659-e8924518e92e · outbound
This paper cites Neue herleitung und explizite restabschätzung der riemann-siegel-formel.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Neue herleitung und explizite restabschätzung der riemann-siegel-formel
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4e54b56a-9c20-43de-8a9a-b75c4fc3e986 · outbound
This paper cites Large ball probabilities, Gaussian comparison and anti-concentration.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Large ball probabilities, Gaussian comparison and anti-concentration
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 80e178fa-782e-42e1-91b8-8b1a8d904fb5 · outbound
This paper cites Tight analyses for non-smooth stochastic gradient descent.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Tight analyses for non-smooth stochastic gradient descent
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f8cfb203-4efa-4635-82ce-6ccfe85cd005 · outbound
This paper cites Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization
Reference 21
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9c08efe7-0248-4fda-82e7-319d3f7e0300 · outbound
This paper cites Stochastic approximation and recursive algorithms and applications, volume 35.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Stochastic approximation and recursive algorithms and applications, volume 35
Reference 22
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7e3d8779-dbec-4327-8343-5c4c414aa135 · outbound
This paper cites First-order and Stochastic Optimization Methods for Machine Learning.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent First-order and Stochastic Optimization Methods for Machine Learning
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation 3ee64b6a-565e-49b2-acec-eebd049214e4 · outbound
This paper cites Root-sgd: Sharp nonasymptotics and asymptotic efficiency in a single algorithm.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Root-sgd: Sharp nonasymptotics and asymptotic efficiency in a single algorithm
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 88aaba79-5e5d-492a-8cab-70cd9b6091c0 · outbound
This paper cites Statistical estimation and online inference via local sgd.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Statistical estimation and online inference via local sgd
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 8351a93b-2e01-4da0-9184-5dafe6fca5d1 · outbound
This paper cites Non-asymptotic analysis of stochastic approximation algo- rithms for machine learning.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Non-asymptotic analysis of stochastic approximation algo- rithms for machine learning
Reference 26
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 60aa9573-af5a-4592-b801-7a6f03d0753f · outbound
This paper cites Robust stochastic approximation approach to stochastic programming.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Robust stochastic approximation approach to stochastic programming
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 11644228-1024-4a9f-a70a-b2ded578ef81 · outbound
This paper cites Nesterov.Introductory Lectures on Convex Optimization: A Basic Course.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Nesterov.Introductory Lectures on Convex Optimization: A Basic Course
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation edda0fe6-2571-4290-b1bd-733baace5aae · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation 5f4fc091-66a8-4102-88b4-71647d7ccc51 · outbound
This paper cites Sharp martingale and semimartingale inequalities, volume 72.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Sharp martingale and semimartingale inequalities, volume 72
Reference 30
- Resolution
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Source-reported events for the cited work
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Observation e2c9ea8d-e32e-4461-8cd4-afa503be7b60 · outbound
This paper cites Acceleration of stochastic approximation by averaging.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Acceleration of stochastic approximation by averaging
Reference 31
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Observation 8435a94f-a446-4d08-b83b-2e311534aa26 · outbound
This paper cites On the momentum term in gradient descent learning algorithms.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent On the momentum term in gradient descent learning algorithms
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 390ace19-0510-4197-af72-e030021b75bb · outbound
This paper cites Making gradient descent optimal for strongly convex stochastic optimization.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Making gradient descent optimal for strongly convex stochastic optimization
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 4a9359e8-4e10-4a61-bc33-34004418732b · outbound
This paper cites Efficient estimations from a slowly convergent Robbins-Monro process.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Efficient estimations from a slowly convergent Robbins-Monro process
Reference 34
- Resolution
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Source-reported events for the cited work
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Observation 7da73397-3522-4d0d-8f24-4aaae01976c8 · outbound
This paper cites High-probability bounds for stochastic optimization and variational inequalities: the case of unbounded variance.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent High-probability bounds for stochastic optimization and variational inequalities: the case of unbounded variance
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation c51ae10f-65ec-4dc0-9e8d-ceeb2ba7c9cf · outbound
This paper cites Gaus- sian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Gaus- sian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
Reference 36
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a48d4510-5d01-4972-b05f-90bcf3cbe0d0 · outbound
This paper cites Minimizing finite sums with the stochastic average gradient.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Minimizing finite sums with the stochastic average gradient
Reference 37
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 60f272d1-3190-4922-85f4-8731aba37809 · outbound
This paper cites Mathematical statistics.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Mathematical statistics
Reference 38
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 55d5a75f-b289-467a-af1c-b41704de8afd · outbound
This paper cites The Jackknife and Bootstrap.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent The Jackknife and Bootstrap
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d355b174-4821-438c-943a-df492fb0ec9b · outbound
This paper cites Berry–Esseen bounds for multivariate nonlinear statis- tics with applications to M-estimators and stochastic gradient descent algorithms.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Berry–Esseen bounds for multivariate nonlinear statis- tics with applications to M-estimators and stochastic gradient descent algorithms
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a384c0c6-29da-487d-adfc-7a79ebc93bb4 · outbound
This paper cites Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:32:28.312001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9c7e51eb-229d-4670-984a-e8e900529de3 · outbound
This paper cites Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD learning.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD learning
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:32:28.317596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 54435c9d-b4d4-4028-88f6-e91931fb2e9e · outbound
This paper cites An introduction to matrix concentration inequalities.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent An introduction to matrix concentration inequalities
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0c02fb04-f828-41c1-93de-eac791acd6b2 · outbound
This paper cites Statistical Inference for Policy Evaluation with Temporal Difference Learning.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Statistical Inference for Policy Evaluation with Temporal Difference Learning
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-06-23T03:13:09.455495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f25336e1-af5e-41ce-a08a-6501b8650305 · outbound
This paper cites Online Bootstrap Inference with Nonconvex Stochastic Gradient Descent Estimator.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Online Bootstrap Inference with Nonconvex Stochastic Gradient Descent Estimator
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:32:28.306490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c144507b-6657-4c84-b3fe-545612f814a5 · outbound
This paper cites Online Covariance Matrix Estimation in Stochastic Gradient Descent.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Online Covariance Matrix Estimation in Stochastic Gradient Descent
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:55:23.077036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5812b0cf-a611-45d9-a01f-4aaf153c7229 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 47
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:57:30.828864Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5f114320-ee65-4a26-a2ad-554697b9105b · outbound
This paper cites We now proceed withPn−1 i=1 E1/2[∥Dn,2 − D(i) n,2∥2].
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent We now proceed withPn−1 i=1 E1/2[∥Dn,2 − D(i) n,2∥2]
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:55:23.108754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ac304ad3-10d6-4eb2-85d0-5aebf9958722 · outbound
This paper cites For k > i , applying A7 and A1, we have E[∥θ(i) k − θk∥2|Fk−1] ≤ ∥θ(i) k−1 − θk−1∥2 − 2αk⟨θ(i) k−1 − θk−1, ∇f(θ(i) k−1) − ∇f(θk−1)⟩ + 2α2 k(L1 + L2)2∥θ(i) k−1 − θk−1∥2.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent For k > i , applying A7 and A1, we have E[∥θ(i) k − θk∥2|Fk−1] ≤ ∥θ(i) k−1 − θk−1∥2 − 2αk⟨θ(i) k−1 − θk−1, ∇f(θ(i) k−1) − ∇f(θk−1)⟩ + 2α2 k(L1 + L2)2∥θ(i) k−1 − θk−1∥2
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:57:30.809178Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation afdef154-f1b9-49b6-bc9e-bcb8f80b9321 · outbound
This paper cites For k > i we denote δ(i) k = ∥θ(i) k − θk∥, similar to (62), we obtain E[{δ(i) k }4|Fk−1] ≤ (1 − 4µαk + 4α2 k(L1 + L2)2(1 + 3c0(L1 + L2))2){δ(i) k−1}4.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent For k > i we denote δ(i) k = ∥θ(i) k − θk∥, similar to (62), we obtain E[{δ(i) k }4|Fk−1] ≤ (1 − 4µαk + 4α2 k(L1 + L2)2(1 + 3c0(L1 + L2))2){δ(i) k−1}4
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:55:23.125981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 64312542-549b-43d9-b659-16967a3aecb9 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:55:23.129325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 923518ba-e490-433f-9966-d5f3df22de3e · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:57:30.867355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b78a60c7-67bf-4065-ac19-bdd41d7bdf06 · outbound
This paper cites (54) Using the elementary bound 1 + t ≤ et for any t ∈ R, we get A1,k ≤ exp −(3/2)µ kX i=1 αi exp 2L2 2 kX i=1 α2 i.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent (54) Using the elementary bound 1 + t ≤ et for any t ∈ R, we get A1,k ≤ exp −(3/2)µ kX i=1 αi exp 2L2 2 kX i=1 α2 i
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:55:23.106720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 938c6c85-3e99-4d15-8b5b-158f1f248a2d · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:55:23.109932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a6e86149-a1a4-44be-98a9-c451e0a7fb05 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 55
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:55:23.061337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ec01cb14-c3d3-4178-b4a5-643f883099b7 · outbound
This paper cites Note, that for k ≤ k1, αk ≥ µ/(4L2 2), hence, we have kY j=k1+1 (1 − αjµ) ≤ exp −µ kX i=1 αi exp µ k1X i=1 αi ≤ exp −µ kX i=1 αi exp 4L2 2 k1X i=1 α2 i.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Note, that for k ≤ k1, αk ≥ µ/(4L2 2), hence, we have kY j=k1+1 (1 − αjµ) ≤ exp −µ kX i=1 αi exp µ k1X i=1 αi ≤ exp −µ kX i=1 αi exp 4L2 2 k1X i=1 α2 i
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:57:30.817060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1c5a1d1a-282c-4141-a548-3768a150a8f3 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 57
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:57:30.805419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 86b2bc68-3355-4ffe-a864-11080a77da36 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 58
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:57:30.832719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3ae67262-768f-40ac-8537-7602ae834dcd · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 59
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:55:23.135323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9e7966e2-7fd4-4b41-b3b2-46eefbd0bc03 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:55:23.165894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e2652f66-34db-4295-b4ad-261112c31d05 · outbound
This paper cites an unresolved cited work.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Unresolved cited work
Reference 61
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T03:57:30.845248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fd46969e-4f59-455a-8d8a-f1324b1b021f · outbound
This paper cites It remains to note that E[∥θk − θ⋆∥2p] ≤ 22p−1E[∥θ′ k − θ⋆∥2p] + 22p−1E[∥θk − θ′ k∥2p] ≤ C2p,1 exp − pµc0 4 (k + k0)1−γ (∥θ0 − θ⋆∥2p + σ2p 2p) + C2p,2σ2p 2pαp k.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent It remains to note that E[∥θk − θ⋆∥2p] ≤ 22p−1E[∥θ′ k − θ⋆∥2p] + 22p−1E[∥θk − θ′ k∥2p] ≤ C2p,1 exp − pµc0 4 (k + k0)1−γ (∥θ0 − θ⋆∥2p + σ2p 2p) + C2p,2σ2p 2pαp k
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:55:23.122351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d9a81935-c47d-4bc6-94ee-6d5d45734ffc · outbound
This paper cites Here C1 and C2 are defined in Lemma 12 and c2,4 = exp exp 10c0(L1 + L2) 16(L1 + L2)2 2γ − 1 + 1 exp 2µc0 1 − γ k1−γ 0 1 3γ − 1 , c2,5 = 21+2γ 2µc0.
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent Here C1 and C2 are defined in Lemma 12 and c2,4 = exp exp 10c0(L1 + L2) 16(L1 + L2)2 2γ − 1 + 1 exp 2µc0 1 − γ k1−γ 0 1 3γ − 1 , c2,5 = 21+2γ 2µc0
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T03:57:30.853413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation 2c59474f-08a3-450c-a8ca-caa6e2161aae · inbound
Gaussian Approximation for Asynchronous Q-learning cites this paper.
Gaussian Approximation for Asynchronous Q-learning Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T07:25:59.959150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8e0a506b-37b2-40f7-9b89-25f83416f3f3 · inbound
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction cites this paper.
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 6
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T15:21:07.934349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4e5af523-a176-4ca5-aa85-cc4e861dd05e · 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 Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T21:31:14.316151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 71f9fac5-4741-4776-bb68-87c373122e96 · inbound
On Gaussian approximation for entropy-regularized Q-learning with function approximation cites this paper.
On Gaussian approximation for entropy-regularized Q-learning with function approximation Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-05-19T22:12:50.620201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ea8226b2-443b-45c2-aa4c-d19fd790edae · inbound
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation cites this paper.
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 26
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T02:12:58.468986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2c7c4f9d-7440-4b55-904f-d47c31a8140f · inbound
Statistical Inference on Gradient Flows cites this paper.
Statistical Inference on Gradient Flows Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Reference 58
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-01T21:46:15.186019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.