Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 10 inbound Pith citation observations for arXiv:2502.07923.

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

pith.paper-citation-record.v1
2502.07923 v2

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:35:29.806274Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:58:33.061678Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T18:35:00.445011Z

Reference resolution

81 of 81 outbound references displayed

verified exact3
verified fuzzy47
unresolved30
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8933ab5-e7d3-4e6b-b6e0-6039b12e426f · outbound

This paper cites Differentially private learning with adaptive clipping.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Differentially private learning with adaptive clipping

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58a0fa68-50e3-4201-a9cb-06ae3765bd3e · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Lower bounds for non-convex stochastic optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.564005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee542676-78ba-4ff9-8502-06b959be78f0 · outbound

This paper cites High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:35:29.998575Z

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Observation 572cd5c0-e405-444e-b68b-2cfb0a13ded0 · outbound

This paper cites Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc48a6c-7a0b-4c1a-967f-c21f04b600af · outbound

This paper cites signsgd: Compressed optimisation for non-convex problems.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness signsgd: Compressed optimisation for non-convex problems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.575843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b1e8e1e-9898-4952-ac4d-d6d078ec5b26 · outbound

This paper cites signSGD with Majority Vote is Communication Efficient And Fault Tolerant.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness signSGD with Majority Vote is Communication Efficient And Fault Tolerant

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.579143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f97dab42-6112-42f8-8920-2f320cadcbe2 · outbound

This paper cites Stochastic gradient descent tricks.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic gradient descent tricks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.511987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation be3fe7cc-b260-44da-9477-3ad46e72e5e7 · outbound

This paper cites Convex optimization.

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

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.502917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fab60b58-38e9-4657-903f-b26aa3b013ef · outbound

This paper cites Libsvm: a library for support vector machines.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Libsvm: a library for support vector machines

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.492612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ddcf3035-a74e-4c46-adf6-c2d3d578843d · outbound

This paper cites Understanding gradient clipping in private sgd: A geometric perspective.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Understanding gradient clipping in private sgd: A geometric perspective

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.482865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82ac2c21-be7c-477b-8c57-b59ad104d75d · outbound

This paper cites Generalized-smooth nonconvex op- timization is as efficient as smooth nonconvex optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Generalized-smooth nonconvex op- timization is as efficient as smooth nonconvex optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.473653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d68cc4a-0b74-4fb3-aa08-7b4448e3adee · outbound

This paper cites Optimal mean estimation without a variance.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Optimal mean estimation without a variance

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.463374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99cd7cb5-4417-45a4-a0d7-9eb503ac8961 · outbound

This paper cites Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:35:29.969935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 160b0a5a-5451-4262-976f-343d284dff67 · outbound

This paper cites Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.606079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e72220c-993f-4772-9b93-649cc8a786a5 · outbound

This paper cites Complexity lower bounds of adaptive gradient algorithms for non-convex stochastic optimization under relaxed smoothness, 2025.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Complexity lower bounds of adaptive gradient algorithms for non-convex stochastic optimization under relaxed smoothness, 2025

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.452870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45ce3836-4636-4cdc-8e6c-490727c64d40 · outbound

This paper cites Robustness to unbounded smoothness of generalized signsgd.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Robustness to unbounded smoothness of generalized signsgd

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.442320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a29ec436-39cc-4bfb-b6ad-ed121065a7bf · outbound

This paper cites Momentum improves normalized sgd.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Momentum improves normalized sgd

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.432366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18f7d251-39df-48e0-87b4-284284319bc1 · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness High-probability bounds for non-convex stochastic opti- mization with heavy tails

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.423130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eefcf3c0-d753-4807-8b95-7ae83150bc5a · outbound

This paper cites From low probability to high confidence in stochastic convex optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness From low probability to high confidence in stochastic convex optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.413699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4e99507-b3f1-4b93-881d-b17c158d1b5e · outbound

This paper cites The gauss–tchebyshev inequality for uni- modal distributions.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness The gauss–tchebyshev inequality for uni- modal distributions

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.404532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05ecdf65-b00f-45d2-bcb8-7303182d3ece · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.628012Z

Source-reported events for the cited work

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Observation 17f8e85c-702f-49c6-a56a-7f11a7a45f2c · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.390023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e1c95593-97aa-4359-afdc-aff198f90dcf · outbound

This paper cites A nearly optimal single loop algorithm for stochastic bilevel optimization under unbounded smoothness.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness A nearly optimal single loop algorithm for stochastic bilevel optimization under unbounded smoothness

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.380409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 663ebf67-f20f-4739-bb75-75d632ed4c0f · outbound

This paper cites Deep learning.

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

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.636582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a67727d9-e98d-4039-a728-7dbdbdca553d · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic optimization with heavy-tailed noise via accelerated gradient clipping.Advances in Neural Information Processing Systems, 33:15042–15053, 2020

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f71baf-9fff-4f0d-9ee9-5b1bda34b963 · outbound

This paper cites Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.642173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f54ba7-e408-45ab-a39f-e19c77899167 · outbound

This paper cites The heavy-tail phenomenon in sgd.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness The heavy-tail phenomenon in sgd

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.358657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bf6f42d5-9dc5-4876-9f6e-ab8124119487 · outbound

This paper cites Bilevel optimization under unbounded smoothness: A new algorithm and convergence analysis.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Bilevel optimization under unbounded smoothness: A new algorithm and convergence analysis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.349201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62852387-2513-46d2-a83a-22189f814b4c · outbound

This paper cites Beyond convexity: Stochastic quasi-convex optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Beyond convexity: Stochastic quasi-convex optimization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.339711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 753df5e4-3ce7-4448-a215-f3f8b3a16286 · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness From Gradient Clipping to Normalization for Heavy Tailed SGD

Reference 30

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Source-reported events for the cited work

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Observation f36c873f-a420-4696-ad68-ba357b994689 · outbound

This paper cites Parameter-agnostic optimization under relaxed smoothness.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Parameter-agnostic optimization under relaxed smoothness

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.330256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7688429f-b71a-4e89-b4ab-0165f55e77d0 · outbound

This paper cites Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Nonlinear gradient mappings and stochastic optimization: A general framework with applications to heavy-tail noise

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.320724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4eb06c58-441d-4890-9870-f52cc63ba4aa · outbound

This paper cites Non-convex distributionally robust optimization: Non-asymptotic analysis.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Non-convex distributionally robust optimization: Non-asymptotic analysis

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.311641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18de87ef-29fd-40ad-a805-85677420daf0 · outbound

This paper cites Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 34

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Observation e01e336b-d4ef-4d02-813b-d2749555b291 · outbound

This paper cites Learning from history for byzantine robust optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Learning from history for byzantine robust optimization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.302706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9afd8804-6d37-4290-8983-232d7b761f78 · outbound

This paper cites Error feedback fixes signsgd and other gradient compression schemes.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Error feedback fixes signsgd and other gradient compression schemes

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.671879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d71ba791-19e9-4ef4-b2a9-e3780723477c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Adam: A Method for Stochastic Optimization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.674918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5517e50-1f3e-4f62-b653-ac073fa704a0 · outbound

This paper cites Revisiting gradient clipping: Stochastic bias and tight convergence guarantees.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Revisiting gradient clipping: Stochastic bias and tight convergence guarantees

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.286468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0822eae1-6ac7-4492-bf48-004778eaa555 · outbound

This paper cites Accelerated zeroth-order method for non-smooth stochastic convex optimization problem with infinite variance.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Accelerated zeroth-order method for non-smooth stochastic convex optimization problem with infinite variance

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.276016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f944eec7-1764-4229-a081-5d371f89945e · outbound

This paper cites Large-scale methods for distributionally robust optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Large-scale methods for distributionally robust optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.266881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b79c360-516a-4881-9dec-bcd6537aafd9 · outbound

This paper cites Convex and non- convex optimization under generalized smoothness.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Convex and non- convex optimization under generalized smoothness

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.257029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3256861c-0377-4058-8350-5ef35cc2fe8d · outbound

This paper cites Convergence of adam under relaxed assumptions.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Convergence of adam under relaxed assumptions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.247436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3bb1aab8-b3a4-46f1-98c3-aab8b62c63c6 · outbound

This paper cites A High Probability Analysis of Adaptive SGD with Momentum.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness A High Probability Analysis of Adaptive SGD with Momentum

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.692489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3506364-330b-4160-9517-6fda0f1d31f4 · outbound

This paper cites Relora: High- rank training through low-rank updates.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Relora: High- rank training through low-rank updates

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.237219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d939d75-a244-4364-afed-cac1e1ca9c83 · outbound

This paper cites Loss landscapes and optimization in over- parameterized non-linear systems and neural networks.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Loss landscapes and optimization in over- parameterized non-linear systems and neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.226988Z

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Observation 72e43639-1328-4465-b050-4ad1898c8f66 · outbound

This paper cites A communication-efficient distributed gradient clipping algorithm for training deep neural networks.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness A communication-efficient distributed gradient clipping algorithm for training deep neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.217153Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5befed6-959b-4ab2-995f-f62e2cafa272 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.704203Z

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Observation 347903b2-c5a1-42f9-a247-07473692df39 · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Breaking the lower bound with (little) structure: Acceleration in non-convex stochastic optimization with heavy-tailed noise

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.202021Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d1b6af07-b2da-44e6-b3b1-5dc5c3eed863 · outbound

This paper cites Decoupled Weight Decay Regularization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Decoupled Weight Decay Regularization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.709724Z

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Observation 67ccee8a-a559-4109-8bc7-32341e2eab7b · outbound

This paper cites Algorithms of robust stochastic optimization based on mirror descent method.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Algorithms of robust stochastic optimization based on mirror descent method

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.192377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e4a0229b-4e4f-4759-844d-14f1087c7a94 · outbound

This paper cites Robust stochastic approximation approach to stochastic programming.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Robust stochastic approximation approach to stochastic programming

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.715722Z

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Observation 6eecd9a1-45f0-4d88-a812-2f4f22e93e26 · outbound

This paper cites Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tails.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tails

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.718613Z

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Observation 3479780a-6819-4597-bc19-63a9270eef81 · outbound

This paper cites On the difficulty of training recurrent neural networks.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness On the difficulty of training recurrent neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.177253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 02c4e61f-4206-422a-b764-0ea3d8a9373a · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 54

Resolution: malformed identifier; no resolver link, observed 2026-08-08T11:35:29.724539Z

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Observation 966c5df5-6ca0-410f-b24d-a3476850d349 · outbound

This paper cites Breaking the heavy-tailed noise barrier in stochastic optimization problems.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Breaking the heavy-tailed noise barrier in stochastic optimization problems

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.167921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1a08760-a7ee-4e7a-b119-7652394c449f · outbound

This paper cites High probability convergence of clipped distributed dual averaging with heavy-tailed noises.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness High probability convergence of clipped distributed dual averaging with heavy-tailed noises

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.157530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a48f63fc-3f19-4bed-97ef-cb578f79e9f5 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.733045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cafe87d-3759-4195-8127-657e2c5f32d2 · outbound

This paper cites Variance-reduced clipping for non-convex optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Variance-reduced clipping for non-convex optimization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.143278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 10fcf077-862e-47e0-b071-907eedfdae61 · outbound

This paper cites A stochastic approximation method.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness A stochastic approximation method

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.738584Z

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Unavailable: canonical work link unavailable.

Observation 567073aa-8fd9-4f21-b7fa-90e1a2a21d72 · outbound

This paper cites High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:35:29.887783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e229d3e6-ffd4-4183-9b4f-a4672e7726a8 · outbound

This paper cites Stochastic sign descent methods: New algorithms and better theory.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic sign descent methods: New algorithms and better theory

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.127872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 154c885f-b530-4ee2-875d-2b2458d6ac00 · outbound

This paper cites 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech dnns

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.117452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 840d7485-1560-4e50-bbaf-f4c472988716 · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Understanding machine learning: From theory to algorithms

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.749543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bbe2dd3-f9ec-49bd-94d6-97700db81892 · outbound

This paper cites GLU Variants Improve Transformer.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness GLU Variants Improve Transformer

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.752359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9a754d2-5894-4d09-9310-4ebeef5e8337 · outbound

This paper cites A tail-index analysis of stochastic gradient noise in deep neural networks.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness A tail-index analysis of stochastic gradient noise in deep neural networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.103116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2ee1b205-7f63-4a0e-8bda-fbb9c3f5e064 · outbound

This paper cites Momentum ensures convergence of signsgd under weaker assumptions.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Momentum ensures convergence of signsgd under weaker assumptions

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.094235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a686756d-bdb1-4d36-a2b9-3ee09edaaf79 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness LLaMA: Open and Efficient Foundation Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.761565Z

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Unavailable: canonical work link unavailable.

Observation 778b925b-0764-47d7-9d3e-ceca3d4af2d3 · outbound

This paper cites Convergence of adagrad for non-convex objectives: Simple proofs and relaxed assumptions.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Convergence of adagrad for non-convex objectives: Simple proofs and relaxed assumptions

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.084355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57e254e7-b633-4305-8ab9-0f88eeffc9ef · outbound

This paper cites On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.767863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61867539-52f4-4397-aa1b-d882d9ce0da9 · outbound

This paper cites Provable adaptivity of adam under non-uniform smoothness.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Provable adaptivity of adam under non-uniform smoothness

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.771207Z

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Unavailable: canonical work link unavailable.

Observation b7fc47fc-c9ef-4eef-9709-5bb08c920b93 · outbound

This paper cites Two sides of one coin: the limits of untuned sgd and the power of adaptive methods.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Two sides of one coin: the limits of untuned sgd and the power of adaptive methods

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.069347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6fe1d662-cf00-4ed5-94a0-c0f9010db147 · outbound

This paper cites Root mean square layer normalization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Root mean square layer normalization

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.777156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7c9b93-46cb-4bcd-af75-aaebdd25d115 · outbound

This paper cites Improved analysis of clipping algorithms for non-convex optimization.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Improved analysis of clipping algorithms for non-convex optimization

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.055015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b6234a6c-6c11-4222-a26a-7ebf1060f727 · outbound

This paper cites Why gradient clipping accelerates training: A theoretical justification for adaptivity.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.045758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24223a43-2546-4e41-b011-6fc1486bc82c · outbound

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

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 33:15383–15393, 2020

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.036613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6f30be4-74ca-47b0-92db-72ce23a428a1 · outbound

This paper cites MGDA Converges under Generalized Smoothness, Provably.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness MGDA Converges under Generalized Smoothness, Provably

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.788923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64c45bb8-6591-43d5-a03d-7bd07c14bd4e · outbound

This paper cites Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.792126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c06a0683-cccb-4e75-8f15-627151518656 · outbound

This paper cites Deconstructing What Makes a Good Optimizer for Language Models.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Deconstructing What Makes a Good Optimizer for Language Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:29.795327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d558fc9-375d-4f1a-a207-7fd7551f33f1 · outbound

This paper cites On the convergence and improvement of stochastic normalized gradient descent.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness On the convergence and improvement of stochastic normalized gradient descent

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.027248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4adcaaf6-504c-4d16-a01c-9980d3b4cb13 · outbound

This paper cites 1 + ∥⃗ σ∥1 ε κ κ−1 #! , Optimal tuning for ε ≤ 8L0 L1 √ d : T = O ∆Lδ 0d ε2 , γk ≡ q ∆ 20Lδ 0dT , Bk ≡ 16∥⃗ σ∥1 ε κ κ−1 : N = O ∆Lδ 0d ε2.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness 1 + ∥⃗ σ∥1 ε κ κ−1 #! , Optimal tuning for ε ≤ 8L0 L1 √ d : T = O ∆Lδ 0d ε2 , γk ≡ q ∆ 20Lδ 0dT , Bk ≡ 16∥⃗ σ∥1 ε κ κ−1 : N = O ∆Lδ 0d ε2

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.017864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bbc8050e-2cb2-4d9d-822b-0f2d8374a7cd · outbound

This paper cites We trained the model for 100k steps.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness We trained the model for 100k steps

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:35:30.008343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 41278835-c0fe-4962-a92b-2995e3d0bccf · inbound

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise cites this paper.

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T10:58:33.061678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83ee6a9a-555b-4756-86a6-a4dc4ed8ff14 · inbound

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs cites this paper.

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T13:25:35.179592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00cb11f9-5f97-4833-bcff-81ab23f36baa · inbound

High-Probability Convergence Guarantees of Decentralized SGD cites this paper.

High-Probability Convergence Guarantees of Decentralized SGD Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:46:07.850453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f9ef3d5-de1e-4104-b448-5afd608f8b19 · inbound

High-Probability Convergence Guarantees of Decentralized SGD cites this paper.

High-Probability Convergence Guarantees of Decentralized SGD Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:16:34.998210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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

Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-03T04:20:57.128342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9057654-2d38-4ed0-9665-bec19aa20f2d · inbound

When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds cites this paper.

When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:07.726962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a8c23f07-80d2-4090-8c92-0253f030f253 · inbound

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives cites this paper.

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:37:19.221765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 11a0073c-9ee4-4515-b0a2-51e645e29b91 · inbound

LionMuon: Alternating Spectral and Sign Descent for Efficient Training cites this paper.

LionMuon: Alternating Spectral and Sign Descent for Efficient Training Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:28:06.839931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dc04b226-d658-48f2-94b7-b5215330ee41 · inbound

LionMuon: Alternating Spectral and Sign Descent for Efficient Training cites this paper.

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:35:00.446615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aac7424a-6135-467e-995e-1fcd0a569ad4 · inbound

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling cites this paper.

Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:52:44.819197Z

Source-reported events for the cited work

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