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Paper Citation Record · LEDGER

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization

As of 13 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2502.05600.

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

pith.paper-citation-record.v1
2502.05600 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:49:52.651020Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T04:33:10.554853Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.639380Z

Reference resolution

61 of 61 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36443e27-632d-4f5d-866d-93232843b052 · outbound

This paper cites Optimal algorithms for online convex optimization with multi-point bandit feedback.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Optimal algorithms for online convex optimization with multi-point bandit feedback

Reference 1

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verified fuzzy
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Source-reported events for the cited work

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Observation cc0ede2c-1047-4882-88bb-c90ae1dde36c · outbound

This paper cites Zeroth-order (non)-convex stochastic optimization via conditional gradient and gradient updates.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Zeroth-order (non)-convex stochastic optimization via conditional gradient and gradient updates

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 6c808476-21ce-4e71-9a5a-2774c242cef5 · outbound

This paper cites Two-point step size gradient methods.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Two-point step size gradient methods

Reference 3

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Source-reported events for the cited work

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Observation 5b53c678-f3ee-43fd-8b54-eb89d2d0a984 · outbound

This paper cites On the distance between two neural networks and the stability of learning.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization On the distance between two neural networks and the stability of learning

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 79c50953-6838-49b8-8f51-fac545afb56e · outbound

This paper cites Training neural networks for and by interpolation.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Training neural networks for and by interpolation

Reference 5

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Source-reported events for the cited work

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Observation 353b20ce-e4bc-47a6-a66d-bb89d0ac2495 · outbound

This paper cites Online learning with imperfect hints.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Online learning with imperfect hints

Reference 6

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Observation a9eb4f30-1647-41a1-87c8-8755c05bc420 · outbound

This paper cites Making sgd parameter-free.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Making sgd parameter-free

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 0e041fac-feb5-4c12-94a1-e3bb2cfa14fe · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Libsvm: a library for support vector machines

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation daa39421-995d-44a2-8933-0447d4d2b89c · outbound

This paper cites Better parameter-free stochastic optimization with ode updates for coin-betting.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Better parameter-free stochastic optimization with ode updates for coin-betting

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 926d5cdf-f65a-4d11-b96a-ece53bd8c0d1 · outbound

This paper cites Faster gradient-free algorithms for nonsmooth nonconvex stochastic optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Faster gradient-free algorithms for nonsmooth nonconvex stochastic optimization

Reference 10

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Source-reported events for the cited work

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Observation 1ded3382-56d1-4850-ac7f-a68a0891a266 · outbound

This paper cites Black-box reductions for parameter-free online learning in banach spaces.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Black-box reductions for parameter-free online learning in banach spaces

Reference 11

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Unavailable: canonical work link unavailable.

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Observation b8185a1d-15a6-4d6a-ac81-f4ad1cefe6b8 · outbound

This paper cites Learning-rate-free learning by d-adaptation.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Learning-rate-free learning by d-adaptation

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 712939a9-c8cb-4583-8880-78d86c8ea34e · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Adaptive subgradient methods for online learning and stochastic optimization

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1180fe6c-072e-4aca-a99d-33e348fbf2ac · outbound

This paper cites Duchi, Peter L.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Duchi, Peter L

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation d3f7d536-5221-4fb0-91e6-96e7891e1491 · outbound

This paper cites Duchi, Michael I.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Duchi, Michael I

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 14d1b00e-205d-49dc-aeaa-ad097930f4f5 · outbound

This paper cites Probability: theory and examples , volume 49.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Probability: theory and examples , volume 49

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3921b38b-443a-4423-b38c-b22b036c45e0 · outbound

This paper cites Online convex optimization in the bandit setting: gradient descent without a gradient.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4d75941c-75df-4b81-9158-cecc187c771f · outbound

This paper cites The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems

Reference 18

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Observation 58a3a714-5f17-4016-80aa-1e54b55c326f · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 0db60a49-b7f7-4524-8a6d-9a69a7c71d73 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Explaining and Harnessing Adversarial Examples

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 00a7f8f8-a63a-4958-87bf-b46924b3c75f · outbound

This paper cites Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Howard, Aaditya Ramdas, Jon McAuliffe, and Jasjeet Sekhon

Reference 21

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Source-reported events for the cited work

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Observation f80756ce-ce41-4c57-964b-dff24a5439f5 · outbound

This paper cites Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors

Reference 22

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Observation 3be57e68-84bb-4330-8a66-b174f43963c9 · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Dog is SGD’s best friend: A parameter-free dynamic step size schedule

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 5d9ebbb4-67c5-436b-a6dd-002d3f298dd5 · outbound

This paper cites DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cdf2a47a-bdc8-4904-99a8-1f27a62fb9e7 · outbound

This paper cites Parameter-free mirror descent.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Parameter-free mirror descent

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 4a054e7d-17a7-4a92-a3c3-5d84cae457cc · outbound

This paper cites Tuning-Free Stochastic Optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Tuning-Free Stochastic Optimization

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 34593c07-09ae-456a-b95a-eb90a06de1bf · outbound

This paper cites Stochastic estimation of the maximum of a regression function.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Stochastic estimation of the maximum of a regression function

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 054a3d56-fb2e-4785-9644-e4821286377b · outbound

This paper cites Kingma and Jimmy Ba.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Kingma and Jimmy Ba

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 1970435e-a449-404e-9ad3-12ab1903a349 · outbound

This paper cites An algorithm with optimal dimension-dependence for zero-order nonsmooth nonconvex stochastic optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization An algorithm with optimal dimension-dependence for zero-order nonsmooth nonconvex stochastic optimization

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 33eba5b0-4c0f-4fc2-b613-294a55961dd0 · outbound

This paper cites Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation dc27d49f-6d70-4de7-8556-123ac7ef1ae7 · outbound

This paper cites A simple uniformly optimal method without line search for convex optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization A simple uniformly optimal method without line search for convex optimization

Reference 31

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no resolver link, observed 2026-08-08T18:49:52.499732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f217a5b6-8e84-4a05-b1aa-b42156f5002b · outbound

This paper cites Gradient-free methods for deterministic and stochastic nonsmooth nonconvex optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Gradient-free methods for deterministic and stochastic nonsmooth nonconvex optimization

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 05f453e7-34da-4fc0-9886-15ded1f5be23 · outbound

This paper cites A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 0930d8ef-be91-4866-8abe-8e2b6c85ac9c · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.515847Z digest=sha256:00da027444b87d69559a61875032d0c2d09a5f8a37c884480fe419bc34dd92d7

Observation bf3722ba-ab99-47d5-89d0-3cdb437fb0d4 · outbound

This paper cites Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.243696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.521812Z digest=sha256:2f1740f8c685aab415b93d28444e3fa1a41e743a35d4491a1f31c8263612a632

Observation d51b7833-834a-4ed9-8703-ac7a0748b03e · outbound

This paper cites Achieving all with no parameters: Adanormalhedge.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Achieving all with no parameters: Adanormalhedge

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.228746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.526866Z digest=sha256:8a741af6dd7cd993423849dd97429a20b52e5344f7d755dce03ffdaaf9374bd6

Observation f12b0d7b-c90f-4163-b0c1-79578da3fa5c · outbound

This paper cites Simple random search provides a competitive approach to reinforcement learning.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Simple random search provides a competitive approach to reinforcement learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.531774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.531774Z digest=sha256:e7c7e13bc659e14c8899dc62fd2a9fe19c213a76cf7fd8b0497d58b952aef147

Observation 734e02e0-00d2-4898-9b3d-b0b137e62ddc · outbound

This paper cites Lipschitz and comparator-norm adaptivity in online learning.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Lipschitz and comparator-norm adaptivity in online learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.213279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.536874Z digest=sha256:181e6ac8199aba8c1a2d13284539095e27c1087a551ed9e2856a14217832c9be

Observation 8473a882-f7ce-49a1-974d-f399a1fa4c53 · outbound

This paper cites Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Adaptive First-and Zeroth-order Methods for Weakly Convex Stochastic Optimization Problems

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.541953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.541953Z digest=sha256:cad1e267018524c3d38ba20db5506a461581c960ad28e7c76cc62e26778800a3

Observation fcc7e03f-e52c-4c43-85bf-010e8a6fe782 · outbound

This paper cites Random gradient-free minimization of convex functions.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Random gradient-free minimization of convex functions

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.197775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.547016Z digest=sha256:1b371d5c66ebebe00ee1a2cc26ddde3e3b2c83b6a88f9e24f2a62973540ab4f4

Observation 285d6089-0263-454f-9cd3-f15216d509d1 · outbound

This paper cites Coin betting and parameter-free online learning.Advances in Neural Information Processing Systems, 29, 2016.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Coin betting and parameter-free online learning.Advances in Neural Information Processing Systems, 29, 2016

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.181777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.551831Z digest=sha256:ca0950c2ae66077b70fd8863aee34726534bbdb34789b1565914dd86f2671423

Observation 24d1bba4-f3bb-453d-aa36-1a2fe03f891c · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Training deep networks without learning rates through coin betting

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.556495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.556495Z digest=sha256:7b439fdeb95afae9bbe8b65df80918bfb975f4bee567cf2c607ff6e331c862bd

Observation b7f04ed1-5b0f-4876-a75e-bb4660376cb2 · outbound

This paper cites A stochastic line search method with expected complexity analysis.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization A stochastic line search method with expected complexity analysis

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.156096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.561676Z digest=sha256:f4024926abbea3cb50f04c2695f759cef51e09391184f1bac0c2963293a7a650

Observation fc62997b-e804-40c3-b3d6-e2734fb9a2c6 · outbound

This paper cites Introduction to optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Introduction to optimization

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.141341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.566525Z digest=sha256:61527b6e395c13606f9fc8cc671953660cd8f026d7c7145cee5c9ffb0598c6e4

Observation d8ce338d-5279-476f-a4c1-d9b3b770bebc · outbound

This paper cites An optimal structured zeroth-order algorithm for non-smooth optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization An optimal structured zeroth-order algorithm for non-smooth optimization

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.126200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.571180Z digest=sha256:99a3c27f6ed5586528b9f0a8de2e4ec100993fb6c5928c936694ce54944e8994

Observation dd2617a2-bfb0-4695-9587-7920a1fd7f2d · outbound

This paper cites L4: Practical loss-based stepsize adaptation for deep learning.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization L4: Practical loss-based stepsize adaptation for deep learning

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.110659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.575770Z digest=sha256:61cf859c9ae5bfc643e4740a17ebf37912f2745b48ec9b334547765377124379

Observation 8e3803c8-7a80-4b85-9116-d73adb6d00de · outbound

This paper cites Understanding adversarial training: Increasing local stability of supervised models through robust optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Understanding adversarial training: Increasing local stability of supervised models through robust optimization

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.580588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.580588Z digest=sha256:cfe6dd2a70cb6d4320811c55901653686816ef4ae0b312e42169aa64f6c9468f

Observation 085cb9c7-2cc6-43ea-99a8-e864bcd122a8 · outbound

This paper cites Online learning and online convex optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Online learning and online convex optimization

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.085569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.585516Z digest=sha256:a51ea8d2584153aafe0707a500137227a3b157b0fea5e1c46803ef7d441e01af

Observation 98fffc83-472d-4b6d-aaf2-ff2a1ebfa910 · outbound

This paper cites An optimal algorithm for bandit and zero-order convex optimization with two-point feedback.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization An optimal algorithm for bandit and zero-order convex optimization with two-point feedback

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.070911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.590368Z digest=sha256:ecd80f7d1d5d00a7aa1d04f109bdb4a8c120d8af3c784dbbea440102dab288c6

Observation f0f59710-0205-4263-a51c-5cae4f5a2b3b · outbound

This paper cites Adafactor: Adaptive learning rates with sublinear memory cost.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Adafactor: Adaptive learning rates with sublinear memory cost

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.595476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.595476Z digest=sha256:19745ac69270d8aedd38860a66ff95e4cceb41d25de9fb3142800c9c82cd25e7

Observation bc115a12-d3ad-4bd6-abf6-5fe4216d24b9 · outbound

This paper cites No-Regret Algorithms for Unconstrained Online Convex Optimization.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization No-Regret Algorithms for Unconstrained Online Convex Optimization

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:49:52.750138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.600678Z digest=sha256:9f6676b59c1dd2badf69c0cb24eb2c4f1212472c0f6100d132860e36b9dbcb56

Observation d71d5497-ba80-4ca8-9645-f52c1a5bf6d6 · outbound

This paper cites Barzilai-borwein step size for stochastic gradient descent.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Barzilai-borwein step size for stochastic gradient descent

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.045720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.606902Z digest=sha256:ddaa5735f570b95f85cda792c1da68e8ceb7684b53e3a48cc9229d9966630f09

Observation dc951868-083c-4e03-a5ec-5ccb48f8b99e · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.029878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.611779Z digest=sha256:9eb367b3d217d9fdbf438a272f49d92830ae551bb25d2b4579fc2717fb7ef235

Observation 10e11de0-ed7b-44a9-bfee-d88d140b2a23 · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Painless stochastic gradient: Interpolation, line-search, and convergence rates

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:53.011371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.616880Z digest=sha256:e43b16902cecb4b1dbab902a4e19b51453b25f5b71ea69fd262284c04550c746

Observation aa1402f7-232c-4421-b504-95cf30a54ef5 · outbound

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Provable adaptivity of adam under non-uniform smoothness

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:52.995430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.621734Z digest=sha256:83d0f3eeba67671288b506f41ceab07b9f09fa6555255487f6b571f669214d14

Observation 5c529546-8b92-4d29-9093-202f7d805c30 · outbound

This paper cites Generalized polyak step size for first order optimization with momentum.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Generalized polyak step size for first order optimization with momentum

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:52.979118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.626297Z digest=sha256:0f304859735e0804d3b319e54265fac40d91ce9de84fb72c926e51151122e98e

Observation c9ff0660-ef8c-4070-92d9-744581c06cf7 · outbound

This paper cites Finite sample convergence rates of zero-order stochastic optimization methods.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Finite sample convergence rates of zero-order stochastic optimization methods

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:52.961741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.631453Z digest=sha256:8b129be365a45883d61980d66a54f38d2e74e339a2591ac3d3c38e62d754b5f7

Observation 7c4f2928-33fc-4b73-ac9a-9a4e2a62b55c · outbound

This paper cites Large Batch Training of Convolutional Networks.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Large Batch Training of Convolutional Networks

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.635960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.635960Z digest=sha256:87d8313a12218e52cd210a21e0e31c2dc87604fa9cfd95a8b3c4466b33142a90

Observation 7ca883b3-5051-4eaa-8feb-4f2d4c62b912 · outbound

This paper cites On stochastic gradient and subgradient methods with adaptive steplength sequences.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization On stochastic gradient and subgradient methods with adaptive steplength sequences

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T18:49:52.945988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-08T18:49:52.640806Z digest=sha256:9baace6792ba3253dd54c73613d60119a04e7e892937d9f2622ef552d318555c

Observation 8fe9f462-59d6-449d-a397-e640aa3981fc · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization ADADELTA: An Adaptive Learning Rate Method

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.645495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.645495Z digest=sha256:22a95d3f2ce03a9c605770ab31e8daa655f7522276b416d24fd248b5e45319d7

Observation ed6a2b6b-07e1-4ec9-bb3d-90f393f4698a · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization Adam-mini: Use Fewer Learning Rates To Gain More

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.651020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.651020Z digest=sha256:96ddaef3bd71dffdd587277fff04f0e03852e1a2410001836c47f3621ad7f152

Pith citing papers

Observation ae9b23cc-19b6-483d-bdb7-406c239036d5 · inbound

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning cites this paper.

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:08:43.640812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-27T04:33:10.554853Z digest=sha256:43566ba6bd79dcbd1eb4ec52b5ee50dc31746aa0bcf4a7ffd0a36a7f4adc4bf8