Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization | Paper Citation Record

Observation c872434e-6588-4d31-b88b-f01ff1233cf8 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization OPT: Open Pre-trained Transformer Language Models

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This paper cites A stochastic approximation method,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization A stochastic approximation method,

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Unresolved cited work

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This paper cites Robust stochastic approximation approach to stochastic programming,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Robust stochastic approximation approach to stochastic programming,

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

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Stochastic first-and zeroth-order methods for nonconvex stochastic programming,

Reference 5

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Observation af8dd58e-ae79-4af6-98c3-ed660300f127 · outbound

This paper cites Lower bounds for non-convex stochastic optimization,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Lower bounds for non-convex stochastic optimization,

Reference 6

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Observation 75a4c71e-e74a-4e34-8adc-ba3c30b265c2 · outbound

This paper cites Imagenet classification with deep convo- lutional neural networks,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Imagenet classification with deep convo- lutional neural networks,

Reference 7

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Observation 0f81d109-a3d5-4b01-a215-ccb5ddac9b1d · outbound

This paper cites Deep residual learning for image recognition,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Deep residual learning for image recognition,

Reference 8

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Observation 754309e0-1efd-49e1-8b92-cdbf9616877f · outbound

This paper cites BERT: Pre-training of deep bidirec- tional transformers for language understanding,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization BERT: Pre-training of deep bidirec- tional transformers for language understanding,

Reference 9

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Observation 8335058d-9871-4fab-b229-aeb4df32a206 · outbound

This paper cites Training Compute-Optimal Large Language Models,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Training Compute-Optimal Large Language Models,

Reference 10

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Observation 9a10a911-3a46-4e57-8ada-fcb23504c8d1 · outbound

This paper cites Dembo and O.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Dembo and O

Reference 11

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Observation 0ef98f80-6695-44fc-b266-d2cd21e9f1b6 · outbound

This paper cites High probability conver- gence of stochastic gradient methods,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High probability conver- gence of stochastic gradient methods,

Reference 12

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Observation bfad895f-644d-46b5-b70f-d022f7d7e89d · outbound

This paper cites High-probability bounds for non-convex stochastic opti- mization with heavy tails,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High-probability bounds for non-convex stochastic opti- mization with heavy tails,

Reference 13

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Observation 396856df-3f56-4ff8-91ef-e2b9d2d81fa5 · outbound

This paper cites Improved convergence in high probability of clipped gradient methods with heavy tailed noise,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Improved convergence in high probability of clipped gradient methods with heavy tailed noise,

Reference 14

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Observation b4421911-7790-4621-ae77-5c436ef5e2e6 · outbound

This paper cites Optimal High-probability Convergence of Nonlinear SGD under Heavy-tailed Noise via Symmetrization,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Optimal High-probability Convergence of Nonlinear SGD under Heavy-tailed Noise via Symmetrization,

Reference 15

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Observation 28a3e9b4-add9-4611-a215-a3d681a7611d · outbound

This paper cites Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-Tailed Noise and Power of Symme- try,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-Tailed Noise and Power of Symme- try,

Reference 16

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Observation ae534bf0-48f0-4482-ad79-fb7e3be927c6 · outbound

This paper cites Armacki,High-Probability and Large Deviations Techniques for Design and Analysis of Large-Scale and Distributed Learning Systems.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Armacki,High-Probability and Large Deviations Techniques for Design and Analysis of Large-Scale and Distributed Learning Systems

Reference 17

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Observation 2b5e3b03-86de-4bd4-b951-d5071b2f6857 · outbound

This paper cites An optimal method for stochastic composite optimization,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization An optimal method for stochastic composite optimization,

Reference 18

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Observation cbb2ef33-6a8e-4c41-9189-47f27aa300a4 · outbound

This paper cites Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization,

Reference 19

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Observation 5b6dea72-b49f-4779-bbcd-512ee5a3212c · outbound

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

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Tight analyses for non-smooth stochastic gradient descent,

Reference 20

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Observation 0232f455-3402-4b5c-8748-a7083cee9d57 · outbound

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

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization A high probability analysis of adaptive sgd with momentum,

Reference 21

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Observation 2f5dafa8-adb9-4670-aebb-24821d3da842 · outbound

This paper cites Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods,

Reference 22

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Observation 6a8c36bd-cffa-4863-937d-ce2f054e0420 · outbound

This paper cites Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,

Reference 23

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Observation 5e6d2cc4-fa2e-4530-81dd-b9625ba37f25 · outbound

This paper cites High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise

Reference 24

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Observation aaea9517-0813-474e-8200-32c4b9d5038b · outbound

This paper cites High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise

Reference 25

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Observation 634188f8-fbfc-47e9-90ed-a4a9334a170d · outbound

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

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High probability guarantees for nonconvex stochastic gradient descent with heavy tails,

Reference 26

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Observation 36adae79-ec89-4feb-b054-c040ed1ac495 · outbound

This paper cites General Tail Bounds for Non-Smooth Stochastic Mirror Descent.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization General Tail Bounds for Non-Smooth Stochastic Mirror Descent

Reference 27

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Observation 616844c9-c9dd-49f8-9eaf-3ff091d2656c · outbound

This paper cites High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise,

Reference 28

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Observation 3e04a9bb-25be-45d4-b111-38b4538e959a · outbound

This paper cites High-probability bounds for stochastic optimization and vari- ational inequalities: the case of unbounded variance,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High-probability bounds for stochastic optimization and vari- ational inequalities: the case of unbounded variance,

Reference 29

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Observation 45ff2a2c-57d7-46a9-a98f-d001fe3c846a · outbound

This paper cites Breaking the lower bound with (little) structure: Accel- eration in non-convex stochastic optimization with heavy-tailed noise,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Breaking the lower bound with (little) structure: Accel- eration in non-convex stochastic optimization with heavy-tailed noise,

Reference 30

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Observation 826f19e8-bdac-4841-b932-701f19c683ba · outbound

This paper cites From Gradient Clipping to Normalization for Heavy Tailed SGD,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization From Gradient Clipping to Normalization for Heavy Tailed SGD,

Reference 31

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Observation 366e9823-8238-435f-9bdf-87946c3be793 · outbound

This paper cites Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 32

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Observation d52f6695-2656-4cee-9c81-d66e8c586b86 · outbound

This paper cites High- probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent un- der Heavy-tailed Noise,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization High- probability Convergence Bounds for Online Nonlinear Stochastic Gradient Descent un- der Heavy-tailed Noise,

Reference 33

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Observation 4b075620-4284-4f70-8124-49c0aa81d5c5 · outbound

This paper cites Large deviations,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Large deviations,

Reference 34

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Observation 7e22a57e-6853-4812-b480-b6cf1a26c3a1 · outbound

This paper cites Ellis,Entropy, Large Deviations, and Statistical Mechanics.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Ellis,Entropy, Large Deviations, and Statistical Mechanics

Reference 35

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Reference 36

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Reference 37

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Large deviations performance of consensus+innovations distributed detection with non-gaussian observa- tions,

Reference 38

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Large deviations analysis of adaptive distributed detection,

Reference 39

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Observation 446b228b-dda4-44d7-9a09-2ec05d1427b0 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime,

Reference 40

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Distributed Detection Over Adaptive Networks: Refined Asymptotics and the Role of Connectivity,

Reference 41

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Observation 4b18d6d0-7442-4d5e-ab96-f36586e31efd · outbound

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Reference 42

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Observation 12188cf2-ff5c-4c22-8374-bc17d0d2c14b · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Inaccuracy rates for distributed inference over random networks with ap- plications to social learning,

Reference 43

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Observation 1eb92454-0f2a-46a1-9682-467642e0e75b · outbound

This paper cites Matta, V.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Matta, V

Reference 44

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Observation 81591201-fcc0-43e9-8c3c-2a4934de207c · outbound

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Reference 45

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Observation 9ac2d24f-a1ea-492c-bd65-e4188814cea2 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Lindhe,Topics on Large Deviations in Artificial Intelligence

Reference 46

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Observation 0e43e719-8d1a-40b4-936b-cf31bfd0f556 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization On the diffusion approximation of nonconvex stochastic gradient descent,

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Observation 32fd876d-f777-4a1a-b3c4-8301f177e774 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Large deviations rates for stochastic gradient descent with strongly convex functions,

Reference 48

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Observation 59072f50-2d32-4382-879d-7fec25c7a976 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization A large deviations perspective on policy gradient algorithms,

Reference 49

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Observation 669c76c3-55c5-4898-96a8-622aeb9e479d · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization What is the long-run distri- bution of stochastic gradient descent? A large deviations analysis,

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Observation 2aeff284-36a5-48d9-8fe1-4885d9e5280e · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization The global convergence of stochastic gradient descent in non-convex landscapes: Sharp estimates via large devia- tions,

Reference 51

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Observation f832892f-dac9-48db-9d9e-d0b62cace5a7 · outbound

This paper cites Accelerated gradient methods with biased gradient estimates: Risk sensitivity, high-probability guarantees, and large deviation bounds,.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Accelerated gradient methods with biased gradient estimates: Risk sensitivity, high-probability guarantees, and large deviation bounds,

Reference 52

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Observation 6aa95349-002c-47c3-a367-4e4400e45fe3 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization On the difficulty of training recurrent neu- ral networks,

Reference 53

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Observation 5865e802-eb15-4bd7-918f-4706f5d0ed8f · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization A tail-index analysis of stochastic gra- dient noise in deep neural networks,

Reference 54

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Observation ea833266-1915-40cc-a557-9461db7809ec · outbound

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Observation 03903ade-3371-4236-8051-93dfd513b8c7 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization The heavy-tail phenomenon in sgd,

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Why gradient clipping accelerates train- ing: A theoretical justification for adaptivity,

Reference 57

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Understanding Clipping for Feder- ated Learning: Convergence and Client-Level Differential Privacy,

Reference 58

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization LLaMA: Open and Efficient Foundation Language Models

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization DeepSeek-V3 Technical Report

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Safe model-based reinforce- ment learning with stability guarantees,

Reference 61

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization On the almost sure conver- gence of stochastic gradient descent in non-convex problems,

Reference 62

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Efficient and accu- rate estimation of lipschitz constants for deep neural networks,

Reference 63

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization On lipschitz bounds of general convolutional neural networks,

Reference 64

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Lipschitz certificates for layered network struc- tures driven by averaged activation operators,

Reference 65

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Rethinking lipschitz neural networks and certified robustness: A boolean function perspective,

Reference 66

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization The lipschitz constant of self-attention,

Reference 67

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Improved analysis of clipping algorithms for non-convex optimization,

Reference 68

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Convergence of adam under relaxed assump- tions,

Reference 69

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization The price of adaptivity in stochastic convex optimization,

Reference 70

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Making gradient descent optimal for strongly convex stochastic optimization,

Reference 71

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework,

Reference 72

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization An improved analysis of stochastic gradient descent with momentum,

Reference 73

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Distributed Pareto Optimization via Diffusion Strategies,

Reference 74

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Better theory for SGD in the nonconvex world,

Reference 75

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Non- linear gradient mappings and stochastic optimization: A general framework with appli- cations to heavy-tail noise,

Reference 76

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Nonconvex stochastic optimization under heavy-tailed noises: Op- timal convergence without gradient clipping,

Reference 77

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Revisiting gradient normalization and clipping for non- convex sgd under heavy-tailed noise: Necessity, sufficiency, and acceleration,

Reference 78

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Gradient convergence in gradient methods with errors,

Reference 79

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization On the convergence of stochastic gradient descent with adaptive stepsizes,

Reference 80

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Observation dfd0accc-195a-4e82-905c-6b27f460fb31 · outbound

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball,

Reference 81

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Convex and non-convex opti- mization under generalized smoothness,

Reference 82

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Does Standard Nonconvex SGD Really Diverge under Heavy-Tailed Noise?

Reference 83

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Can sgd handle heavy-tailed noise?,

Reference 84

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This paper cites NIST Digital Library of Mathematical Functions.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization NIST Digital Library of Mathematical Functions

Reference 85

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Robust Estimation of a Location Parameter,

Reference 86

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Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Gradient Based Clustering,

Reference 87

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Reference 88

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Reference 89

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Observation a29c322e-dd70-43c1-aed2-814181270649 · outbound

This paper cites Proof.The first claim follows directly from Assumption 3 and Definition 1.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Proof.The first claim follows directly from Assumption 3 and Definition 1

Reference 90

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Reference 91

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Reference 92

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This paper cites Proof.To prove the first part, note that from Assumption 2, the choice of clipping threshold in (42) and the definition ofB p, we have, for anyt≥B p ∥∇f(x t)∥ ≤G≤ γt 2.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Proof.To prove the first part, note that from Assumption 2, the choice of clipping threshold in (42) and the definition ofB p, we have, for anyt≥B p ∥∇f(x t)∥ ≤G≤ γt 2

Reference 93

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Reference 94

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Observation 846f0cbe-969a-4814-a616-e2a1381f28e5 · outbound

This paper cites Proof.Recall that we showed the following inequality in Appendix F, for allk≥1 f(x k+1)≤f(x k)− αk 2 ∥∇f(x k)∥2 −α k⟨∇f(x k), θu k ⟩+ αk 2 ∥θb k∥2 + α2 kγ2 kL 2.

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Proof.Recall that we showed the following inequality in Appendix F, for allk≥1 f(x k+1)≤f(x k)− αk 2 ∥∇f(x k)∥2 −α k⟨∇f(x k), θu k ⟩+ αk 2 ∥θb k∥2 + α2 kγ2 kL 2

Reference 95

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