Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods | Paper Citation Record

Observation 117ef500-7b80-4c93-a9be-d1c2afd38f7e · outbound

This paper cites Katyusha: The first direct acceleration of stochastic gradient methods.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Katyusha: The first direct acceleration of stochastic gradient methods

Reference 1

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This paper cites KatyushaX:Simplemomentummethodforstochasticsum-of-nonconvexoptimization.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods KatyushaX:Simplemomentummethodforstochasticsum-of-nonconvexoptimization

Reference 2

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This paper cites Variance reduction for faster non-convex optimization.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Variance reduction for faster non-convex optimization

Reference 3

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This paper cites On the convergence of nesterov’s accelerated gradient method in stochastic settings.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods On the convergence of nesterov’s accelerated gradient method in stochastic settings

Reference 4

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This paper cites Gradient convergence in gradient methods with errors.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Gradient convergence in gradient methods with errors

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:21.552127Z

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Observation b56cb8c7-d3d3-4599-bd8d-ca47679e4611 · outbound

This paper cites Oxford University Press, 02 2013.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Oxford University Press, 02 2013

Reference 6

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This paper cites High-probability bounds for non-convex stochastic optimization with heavy tails.Advances in Neural Information Processing Systems, 34:4883–4895, 2021.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High-probability bounds for non-convex stochastic optimization with heavy tails.Advances in Neural Information Processing Systems, 34:4883–4895, 2021

Reference 7

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This paper cites Stochastic first order methods in smooth convex optimization.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic first order methods in smooth convex optimization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:21.315177Z

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This paper cites Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 9

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This paper cites The power of adaptivity in sgd: Self-tuning step sizes with unbounded gradients and affine variance.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods The power of adaptivity in sgd: Self-tuning step sizes with unbounded gradients and affine variance

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:21.051339Z

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Observation 653fa8dc-bd1f-4ecd-8751-d68ba5f14259 · outbound

This paper cites Stochastic heavy ball.Electronic Journal of Statistics, 12(1):461–529, 2018.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic heavy ball.Electronic Journal of Statistics, 12(1):461–529, 2018

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:20.866306Z

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Observation c819efeb-4a34-45f0-b96e-e23394cda9b6 · outbound

This paper cites Stabilized SVRG: Simple variance reduction for nonconvex optimization.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stabilized SVRG: Simple variance reduction for nonconvex optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:20.676197Z

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Observation 8859b425-51c2-4f41-84bf-2de8b487ee51 · outbound

This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 13

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Observation 9d6c863b-4a00-4435-8f73-94469f55cc1e · outbound

This paper cites Understanding the role of momentum in stochastic gradient methods.Advances in Neural Information Processing Systems, 32, 2019.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Understanding the role of momentum in stochastic gradient methods.Advances in Neural Information Processing Systems, 32, 2019

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:20.367268Z

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Observation 7a2398fd-95ce-41b5-b67a-43ba9946d9e9 · outbound

This paper cites Goodfellow, Yoshua Bengio, and Aaron Courville.Deep Learning.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Goodfellow, Yoshua Bengio, and Aaron Courville.Deep Learning

Reference 15

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Observation 0b2434b3-f45d-43b0-b82a-28034f50a8a3 · outbound

This paper cites Stochastic optimization with heavy- tailed noise via accelerated gradient clipping.Advances in Neural Information Processing Systems, 33:15042–15053, 2020.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic optimization with heavy- tailed noise via accelerated gradient clipping.Advances in Neural Information Processing Systems, 33:15042–15053, 2020

Reference 16

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Observation d9d7dddd-5078-410b-8ed2-3c827f8ee529 · outbound

This paper cites Tight analyses for non-smooth stochastic gradient descent.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Tight analyses for non-smooth stochastic gradient descent

Reference 17

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Observation c135eb54-c9f6-4b54-80ed-26f72a51579c · outbound

This paper cites Making the last iterate of SGD information theoretically optimal.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Making the last iterate of SGD information theoretically optimal

Reference 18

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Observation f51eeb78-f867-44ec-be48-971d6e0f55cf · outbound

This paper cites Accelerating stochastic gradient descent using predictive variance reduction.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Accelerating stochastic gradient descent using predictive variance reduction

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Observation bf0716f3-ebd8-4b0b-a886-d898db2f3ae4 · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:19.834673Z

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This paper cites High probability bounds for a class of nonconvex algorithms with adagrad stepsize.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High probability bounds for a class of nonconvex algorithms with adagrad stepsize

Reference 21

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Observation b4b09144-7881-43a7-a90e-d123f7b8335e · outbound

This paper cites On the insufficiency of existing momentum schemes for stochastic optimization.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods On the insufficiency of existing momentum schemes for stochastic optimization

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:19.475227Z

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Observation 8b4d9f79-aefa-4a87-b501-87fe4f3f9e89 · outbound

This paper cites Stochastic estimation of the maximum of a regression function.The Annals of Mathematical Statistics, pages 462–466, 1952.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic estimation of the maximum of a regression function.The Annals of Mathematical Statistics, pages 462–466, 1952

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Observation 8e9ff5cc-ea96-449b-9bc6-d8d068110a98 · outbound

This paper cites Kushner and Hai Huang.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Kushner and Hai Huang

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Observation e08f7b85-d87e-4510-9692-eefc137a2f7e · outbound

This paper cites An optimal method for stochastic composite optimization.Mathematical Programming, 133(1-2):365–397, 2012.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods An optimal method for stochastic composite optimization.Mathematical Programming, 133(1-2):365–397, 2012

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Observation 34ca3088-321c-429a-a5d2-98256491880e · outbound

This paper cites Springer, 2020.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Springer, 2020

Reference 26

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This paper cites Validation analysis of mirror descent stochastic approximation method.Mathematical programming, 134(2):425–458, 2012.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Validation analysis of mirror descent stochastic approximation method.Mathematical programming, 134(2):425–458, 2012

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This paper cites High probability guarantees for nonconvex stochastic gradient descent with heavy tails.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High probability guarantees for nonconvex stochastic gradient descent with heavy tails

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Observation d63ad830-ed82-46c0-a991-62283bcb1ef1 · outbound

This paper cites On the convergence of stochastic gradient descent with adaptive stepsizes.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods On the convergence of stochastic gradient descent with adaptive stepsizes

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:18.423525Z

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Observation 0a494767-2a9d-4bd1-92ab-05990cd2e010 · outbound

This paper cites A high probability analysis of adaptive sgd with momentum.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods A high probability analysis of adaptive sgd with momentum

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Observation 41515cc5-7c9d-4229-97f9-f5cfb1401727 · outbound

This paper cites An improved analysis of stochastic gradient descent with momentum.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods An improved analysis of stochastic gradient descent with momentum

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Observation e3c557e4-cc67-40b4-b68a-42ae1b54114d · outbound

This paper cites High probability convergence of stochastic gradient methods.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High probability convergence of stochastic gradient methods

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Observation af95c303-fbb8-41ba-bde3-8d412c531fa2 · outbound

This paper cites Revisiting the last-iterate convergence of stochastic gradient methods.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Revisiting the last-iterate convergence of stochastic gradient methods

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Observation b5ee906e-0e1d-40cf-b860-a6d3b74a9939 · outbound

This paper cites High-probability convergence bounds for non-convex stochastic gradient descent.arXiv preprint arXiv:2006.05610, 2020.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High-probability convergence bounds for non-convex stochastic gradient descent.arXiv preprint arXiv:2006.05610, 2020

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This paper cites Algorithms of robust stochastic optimization based on mirror descent method.Automation and Remote Control, 80:1607–1627, 2019.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Algorithms of robust stochastic optimization based on mirror descent method.Automation and Remote Control, 80:1607–1627, 2019

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Observation e0d91a0c-4cac-4b0c-93ec-7569d6e2e4c4 · outbound

This paper cites Robust stochastic approximation approach to stochastic programming.SIAM Journal on optimization, 19(4):1574–1609, 2009.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Robust stochastic approximation approach to stochastic programming.SIAM Journal on optimization, 19(4):1574–1609, 2009

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:17.933716Z

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Observation 24b9af45-a705-49cd-894e-4444cb898cd6 · outbound

This paper cites Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Early stopping-but when? InNeural Networks: Tricks of the trade, pages 55–69

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This paper cites Robbins and D.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Robbins and D

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Observation 05b5e893-c7c2-4c2f-88fa-fab2bfec96c1 · outbound

This paper cites Rustagi, editor,Optimizing Methods in Statistics, pages 233–257.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Rustagi, editor,Optimizing Methods in Statistics, pages 233–257

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:17.472358Z

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Observation c26f7a77-da2b-4c85-a5b2-97657f3b7eea · outbound

This paper cites A stochastic approximation method.The annals of mathematical statistics, pages 400–407, 1951.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods A stochastic approximation method.The annals of mathematical statistics, pages 400–407, 1951

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Observation 0bd7bf79-e673-45a8-8eb9-29089aa0ac20 · outbound

This paper cites Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:17.258755Z

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Observation dd4c3791-d256-4deb-a187-d3147823b0a9 · outbound

This paper cites Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:17.063281Z

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Observation 3c8b564d-4277-469b-9af9-faa8a7bab1ec · outbound

This paper cites High-dimensional probability: An introduction with applications in data science, volume 47.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods High-dimensional probability: An introduction with applications in data science, volume 47

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Observation 550e24fe-f1ee-4dc0-8409-0c428820a33f · outbound

This paper cites Etude critique de la notion de collectif.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Etude critique de la notion de collectif

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Observation e7e5571a-5c34-4429-8a17-036b0f239bc5 · outbound

This paper cites Cambridge University Press, 2019.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Cambridge University Press, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:16.842013Z

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Observation b72bd498-2e07-4f0e-aad8-262445373e64 · outbound

This paper cites Adagrad stepsizes: Sharp convergence over nonconvex landscapes.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Adagrad stepsizes: Sharp convergence over nonconvex landscapes

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:58:16.655757Z

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Observation d7bd50e3-7bb5-4320-bc73-88e7165168ee · outbound

This paper cites A Unified Analysis of Stochastic Momentum Methods for Deep Learning.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods A Unified Analysis of Stochastic Momentum Methods for Deep Learning

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This paper cites Why are adaptive methods good for attention models?Advances in Neural Information Processing Systems, 33:15383–15393, 2020.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Why are adaptive methods good for attention models?Advances in Neural Information Processing Systems, 33:15383–15393, 2020

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