Paper Citation Record · LEDGER

Online convex optimization in the bandit setting: gradient descent without a gradient

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:cs/0408007.

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

pith.paper-citation-record.v1
cs/0408007 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:50:22.702223Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T10:39:44.911649Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7d863078-e315-4cb9-9e25-3b4d3e74d1ae · inbound

Accelerated zero-order SGD under high-order smoothness and overparameterized regime cites this paper.

Accelerated zero-order SGD under high-order smoothness and overparameterized regime Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T15:50:22.702223Z

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Observation 310683d1-df06-46a5-af0c-1e0ec97ba0bb · inbound

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps cites this paper.

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:45:17.566940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 97f137c5-11ef-4a1a-a999-3c621b822f90 · inbound

Refining Adaptive Zeroth-Order Optimization at Ease cites this paper.

Refining Adaptive Zeroth-Order Optimization at Ease Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T17:00:29.350038Z

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Observation 20a48c14-9dd9-4d01-85ed-f346d38eecbc · inbound

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning cites this paper.

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T12:41:16.266680Z

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

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-08T18:49:52.428331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51750b43-8fb0-49ae-b43e-37b9d84f058d · inbound

Efficient Controllable Diffusion via Optimal Classifier Guidance cites this paper.

Efficient Controllable Diffusion via Optimal Classifier Guidance Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.404690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a54bc48-f247-44ca-973f-7d20c8f2c31d · inbound

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining cites this paper.

Estimating the Effects of Sample Training Orders for Large Language Models without Retraining Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:11.090686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 306390fe-afcb-4056-97a0-8d0d96505c1c · inbound

Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost Asymmetry cites this paper.

Rewriting the Budget: A General Framework for Black-Box Attacks Under Cost Asymmetry Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:57:32.620428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ad7b1f2-9c89-43b7-b40e-cdc93f30d5c3 · inbound

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness cites this paper.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:27:21.851954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36e22ed4-9273-4f71-80ce-5028e4437784 · inbound

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing cites this paper.

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:15.038451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dacf90eb-3583-4c63-90c7-5f8541ced4b5 · inbound

Baryon-antibaryon photoproduction cross sections off the proton cites this paper.

Baryon-antibaryon photoproduction cross sections off the proton Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-18T03:22:20.545304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e98cc4d2-bdfc-4777-a01f-0788a4871840 · inbound

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization cites this paper.

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-21T19:10:30.815457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8bdf5814-b2ae-471f-8fa2-0681b71e2bad · inbound

Policy Optimization for Unknown Systems using Differentiable Model Predictive Control cites this paper.

Policy Optimization for Unknown Systems using Differentiable Model Predictive Control Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-17T22:30:23.300519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 81a17fa4-2a22-45f8-ba6f-807df74e58e5 · inbound

NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning cites this paper.

NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T15:30:07.425842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 41469fcd-3734-4275-bfa7-db03e3e64b1e · inbound

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies cites this paper.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:09:50.572861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4b4c07eb-e9ad-4922-bd82-75b646945edc · inbound

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context cites this paper.

Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 137

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:09:50.572861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f5507636-ce38-4284-94fe-c4f09eeb1163 · inbound

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets cites this paper.

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 195

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:09:50.572861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e246642c-a2a9-400c-9918-9f3bde78f0f0 · inbound

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered cites this paper.

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 111

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T21:23:44.536591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8af85270-fb52-4d0e-a7a0-b3f320d25718 · inbound

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing cites this paper.

Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 70

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T20:18:59.791408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7422edfc-547b-4466-b169-b6b3e160d87d · inbound

Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise cites this paper.

Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T23:22:52.341734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ddf20486-8c00-4698-857a-3181b6b3caf2 · inbound

Online Conformal Prediction with Corrupted Feedback cites this paper.

Online Conformal Prediction with Corrupted Feedback Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-21T07:14:02.588756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 557d85f3-3be1-40f4-9e81-727d865b1bf5 · inbound

Bandit Convex Optimization with Gradient Prediction Adaptivity cites this paper.

Bandit Convex Optimization with Gradient Prediction Adaptivity Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-22T07:36:14.090622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8683924c-60ba-475c-9712-6a7dc598a7b8 · inbound

Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays cites this paper.

Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-25T05:30:22.917825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3890bffa-0af8-4745-9128-7c70e5cbaef4 · inbound

ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design cites this paper.

ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-02T01:36:25.833484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa995d2e-fc65-456e-bb85-165f1115c366 · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T12:06:55.416807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9f3c38f2-3272-4058-b1b7-17bd438f9463 · inbound

Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization cites this paper.

Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T20:47:22.794507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ead2d0d-cc1f-490d-bee5-4bb04159df5b · 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 Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-07-03T17:08:43.684241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1019a2b5-ecd3-4bf4-9d40-28500a6511a1 · inbound

Leveraging Similarities in Multi-Armed Bandits cites this paper.

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-04T10:39:44.913082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a3c017ad-477a-4b0b-829f-de9ed2d466b8 · inbound

Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization cites this paper.

Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization Online convex optimization in the bandit setting: gradient descent without a gradient

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T11:19:21.356983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.