AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent | Paper Citation Record

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This paper cites How Free is Parameter-Free Stochastic Optimization?.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent How Free is Parameter-Free Stochastic Optimization?

Reference 1

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This paper cites Calculus , volume 1.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Calculus , volume 1

Reference 2

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This paper cites Gradient descent converges linearly for logistic regression on separable data.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Gradient descent converges linearly for logistic regression on separable data

Reference 3

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This paper cites Julia: A fresh approach to numerical computing.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Julia: A fresh approach to numerical computing

Reference 4

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This paper cites Making SGD parameter-free.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Making SGD parameter-free

Reference 5

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This paper cites Understanding and detecting convergence for stochastic gradient descent with momentum.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Understanding and detecting convergence for stochastic gradient descent with momentum

Reference 6

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This paper cites Convergence diagnostics for stochastic gradient descent with constant step size.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Convergence diagnostics for stochastic gradient descent with constant step size

Reference 7

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This paper cites Automatically constructing a corpus of sentential paraphrases.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Automatically constructing a corpus of sentential paraphrases

Reference 8

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This paper cites Robust, accurate stochastic optimization for variational inference.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Robust, accurate stochastic optimization for variational inference

Reference 9

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This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Adaptive subgradient methods for online learning and stochastic optimization

Reference 10

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This paper cites Learning-rate-free learning by D - A daptation.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Learning-rate-free learning by D - A daptation

Reference 11

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Observation af7dafda-77e2-414a-8acb-756552a0c142 · outbound

This paper cites Markov Chains.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Markov Chains

Reference 12

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This paper cites Probability: Theory and Examples.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Probability: Theory and Examples

Reference 13

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This paper cites The road less scheduled.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent The road less scheduled

Reference 14

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This paper cites Bayesian Data Analysis.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Bayesian Data Analysis

Reference 15

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This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 16

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This paper cites Inference from iterative simulation using multiple sequences.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Inference from iterative simulation using multiple sequences

Reference 17

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This paper cites Don't be so monotone: R elaxing stochastic line search in over-parameterized models.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Don't be so monotone: R elaxing stochastic line search in over-parameterized models

Reference 18

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Observation 0380042f-3f8f-460a-a8b9-8bc45ae1a90f · outbound

This paper cites Variance-reduced methods for machine learning.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Variance-reduced methods for machine learning

Reference 19

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Observation 36e6b8fe-9663-4636-b810-db23e70870ae · outbound

This paper cites Srivastava, and K.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Srivastava, and K

Reference 20

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This paper cites Deep residual learning for image recognition.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Deep residual learning for image recognition

Reference 21

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This paper cites DoG is SGD 's best friend: A parameter-free dynamic step size schedule.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent DoG is SGD 's best friend: A parameter-free dynamic step size schedule

Reference 22

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This paper cites Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification

Reference 23

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This paper cites Adam: A Method for Stochastic Optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Adam: A Method for Stochastic Optimization

Reference 24

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This paper cites Accelerated parameter-free stochastic optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Accelerated parameter-free stochastic optimization

Reference 25

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This paper cites Tuning-Free Stochastic Optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Tuning-Free Stochastic Optimization

Reference 26

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This paper cites Linear convergence of black-box variational inference: S hould we stick the landing? In International Conference on Artificial Intelligence and Statistics , pages 235--243.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Linear convergence of black-box variational inference: S hould we stick the landing? In International Conference on Artificial Intelligence and Statistics , pages 235--243

Reference 27

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This paper cites DoWG unleashed: A n efficient universal parameter-free gradient descent method.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent DoWG unleashed: A n efficient universal parameter-free gradient descent method

Reference 28

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This paper cites Learning multiple layers of features from tiny images.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Learning multiple layers of features from tiny images

Reference 29

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 30

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This paper cites Stochastic polyak step-size for SGD : A n adaptive learning rate for fast convergence.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Stochastic polyak step-size for SGD : A n adaptive learning rate for fast convergence

Reference 31

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This paper cites Using statistics to automate stochastic optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Using statistics to automate stochastic optimization

Reference 32

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This paper cites Prodigy: An Expeditiously Adaptive Parameter-Free Learner.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Prodigy: An Expeditiously Adaptive Parameter-Free Learner

Reference 33

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This paper cites Adaptive Gradient Descent without Descent.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Adaptive Gradient Descent without Descent

Reference 34

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This paper cites Beating SGD saturation with tail-averaging and minibatching.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Beating SGD saturation with tail-averaging and minibatching

Reference 35

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This paper cites Let's make block coordinate descent converge faster: F aster greedy rules, message-passing, active-set complexity, and superlinear convergence.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Let's make block coordinate descent converge faster: F aster greedy rules, message-passing, active-set complexity, and superlinear convergence

Reference 36

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This paper cites Dynamics of SGD with stochastic P olyak stepsizes: T ruly adaptive variants and convergence to exact solution.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Dynamics of SGD with stochastic P olyak stepsizes: T ruly adaptive variants and convergence to exact solution

Reference 37

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This paper cites Training deep networks without learning rates through coin betting.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Training deep networks without learning rates through coin betting

Reference 38

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This paper cites On convergence-diagnostic based step sizes for stochastic gradient descent.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent On convergence-diagnostic based step sizes for stochastic gradient descent

Reference 39

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This paper cites On the determination of the step size in stochastic quasigradient methods.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent On the determination of the step size in stochastic quasigradient methods

Reference 40

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This paper cites Non-asymptotic confidence bounds for stochastic approximation algorithms with constant step size.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Non-asymptotic confidence bounds for stochastic approximation algorithms with constant step size

Reference 41

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This paper cites Py T orch: A n imperative style, high-performance deep learning library.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Py T orch: A n imperative style, high-performance deep learning library

Reference 42

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This paper cites https://huggingface.co/microsoft/resnet-18, 2025.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent https://huggingface.co/microsoft/resnet-18, 2025

Reference 43

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This paper cites A stochastic approximation method.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent A stochastic approximation method

Reference 44

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This paper cites https://huggingface.co/FacebookAI/roberta-base, 2025.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent https://huggingface.co/FacebookAI/roberta-base, 2025

Reference 45

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This paper cites Anytime Tail Averaging.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Anytime Tail Averaging

Reference 46

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Observation dbfa0b42-1d36-4900-9839-ef3ce529f51a · outbound

This paper cites Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Reference 47

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This paper cites Sticking the landing: S imple, lower-variance gradient estimators for variational inference.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Sticking the landing: S imple, lower-variance gradient estimators for variational inference

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.347644Z

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This paper cites SQuAD : 100,000+ questions for machine comprehension of text.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent SQuAD : 100,000+ questions for machine comprehension of text

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:31.130714Z

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This paper cites Virtual library of simulation experiments: T est functions and datasets, 2013.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Virtual library of simulation experiments: T est functions and datasets, 2013

Reference 50

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Recursive deep models for semantic compositionality over a sentiment treebank

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.762131Z

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Observation b597a55d-446a-46dd-83f6-c4175b93c23b · outbound

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

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Stochastic gradient descent for non-smooth optimization: C onvergence results and optimal averaging schemes

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.583910Z

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Observation f81a50cd-0413-41b8-9408-88f6d4dd081f · outbound

This paper cites Painless stochastic gradient: I nterpolation, line-search, and convergence rates.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Painless stochastic gradient: I nterpolation, line-search, and convergence rates

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.444768Z

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Observation 19139d0b-134b-46c5-a154-68bf21d15add · outbound

This paper cites A framework for improving the reliability of black-box variational inference.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent A framework for improving the reliability of black-box variational inference

Reference 54

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This paper cites GLUE : A multi-task benchmark and analysis platform for natural language understanding.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent GLUE : A multi-task benchmark and analysis platform for natural language understanding

Reference 55

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This paper cites Fluctuation-dissipation relations for stochastic gradient descent.

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent Fluctuation-dissipation relations for stochastic gradient descent

Reference 56

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