Paper Citation Record · LEDGER

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization

As of 22 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.25480.

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

pith.paper-citation-record.v1
2607.25480 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:23:42.290421Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50d875b7-2491-4e4c-9aa0-84a73b00cb12 · outbound

This paper cites Projection-free bandit convex optimization.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Projection-free bandit convex optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.493470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ff45993-95df-49a4-93a8-bbee5c3d101c · outbound

This paper cites Figure 3 shows the percentage contribution of gradient refinement versus Polyak correction for each problem.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Figure 3 shows the percentage contribution of gradient refinement versus Polyak correction for each problem

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:42.229950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39c0469b-208e-47d8-a36d-0c1331cae96c · outbound

This paper cites Online Convex Optimization with Time-Varying Constraints.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Online Convex Optimization with Time-Varying Constraints

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.675720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ed73314-8105-48be-a5e7-fddb82708117 · outbound

This paper cites generalized Pythagorean projection.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization generalized Pythagorean projection

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.899090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8da2a9b-b40c-47c7-9a13-4d0b3fe77f86 · outbound

This paper cites Sinceg(v) =−ρ >−ϵ, we havev /∈C ϵ.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Sinceg(v) =−ρ >−ϵ, we havev /∈C ϵ

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.974236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51a9b6df-b015-453c-9209-b67586353a87 · outbound

This paper cites Therefore(B) = c 2 ∑ tbt/√St≤c (√GT +ϵ 0−√ϵ0 ).

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Therefore(B) = c 2 ∑ tbt/√St≤c (√GT +ϵ 0−√ϵ0 )

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:42.070050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6bb2794-89d4-4485-a6d1-fd6e78966fc6 · outbound

This paper cites an unresolved cited work.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:42.156105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3749f7b4-42b6-4c70-92d3-7654dfad3691 · outbound

This paper cites B.3 Sensitivity Analysis We examine how the bound improvement varies with the step sizeηand the shrinkage parameterρ.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization B.3 Sensitivity Analysis We examine how the bound improvement varies with the step sizeηand the shrinkage parameterρ

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-01T02:23:42.290421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66115df6-63f0-4e5e-bf68-71e6dba6a095 · outbound

This paper cites Jacob Steinhardt and Percy Liang.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Jacob Steinhardt and Percy Liang

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.751230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd93e9df-9e94-419e-9b07-9cb0bdb6c17a · outbound

This paper cites 16 Elad Hazan and Satyen Kale.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization 16 Elad Hazan and Satyen Kale

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.592676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9a82ddc-c9ff-4af5-b643-5086c7e72f38 · outbound

This paper cites Amir Beck.First-order methods in optimization.

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Amir Beck.First-order methods in optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.433965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6022178-133b-40b8-a3e0-3bd9880bc59b · outbound

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

Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-01T02:23:41.823447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.