Paper Citation Record · LEDGER

Contextual Bandit Optimization with Pre-Trained Neural Networks

As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2501.06258.

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

pith.paper-citation-record.v1
2501.06258 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:34:10.717235Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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 fuzzy4
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b739e923-7477-40af-82e6-94efd5a96459 · outbound

This paper cites Complexity regularization for squared error loss, 2007.

Contextual Bandit Optimization with Pre-Trained Neural Networks Complexity regularization for squared error loss, 2007

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:34:10.907552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 562e4ecf-1dd9-4a40-91a8-ef2324e42925 · outbound

This paper cites Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.

Contextual Bandit Optimization with Pre-Trained Neural Networks Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.670000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87656331-4796-4c35-890d-17f1864efcdb · outbound

This paper cites Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling.

Contextual Bandit Optimization with Pre-Trained Neural Networks Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.674867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7324a2b-ced8-4bde-962a-997d9fba396f · outbound

This paper cites Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond.

Contextual Bandit Optimization with Pre-Trained Neural Networks Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.679912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85f461bc-fd2e-4bbb-9ad4-a76bf861c197 · outbound

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

Contextual Bandit Optimization with Pre-Trained Neural Networks Understanding machine learning: From theory to algorithms

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.685210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae8a0753-f82b-417a-86bd-6c51defefe32 · outbound

This paper cites Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.

Contextual Bandit Optimization with Pre-Trained Neural Networks Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.689747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4592e48f-7b96-4825-8a54-45dfe2596925 · outbound

This paper cites The bitter lesson.

Contextual Bandit Optimization with Pre-Trained Neural Networks The bitter lesson

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:34:10.881596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 970c0128-82ef-447d-8c7d-97f1bc2dbd4b · outbound

This paper cites High-dimensional statistics: A non-asymptotic view- point, volume 48.

Contextual Bandit Optimization with Pre-Trained Neural Networks High-dimensional statistics: A non-asymptotic view- point, volume 48

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:34:10.867341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 235bd3e7-b4f3-481b-ba42-e3e768248ee0 · outbound

This paper cites Neural Contextual Bandits with Deep Representation and Shallow Exploration.

Contextual Bandit Optimization with Pre-Trained Neural Networks Neural Contextual Bandits with Deep Representation and Shallow Exploration

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.703817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0ff7f27-7efa-4e50-9e1c-6f8a7bc39d7a · outbound

This paper cites Pyhessian: Neural networks through the lens of the hessian.

Contextual Bandit Optimization with Pre-Trained Neural Networks Pyhessian: Neural networks through the lens of the hessian

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:34:10.849164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc984855-21ad-4262-a47f-4472ed617441 · outbound

This paper cites Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback.

Contextual Bandit Optimization with Pre-Trained Neural Networks Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.712466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09cc7512-7735-441c-826e-87fcabac08ca · outbound

This paper cites Neural Thompson Sampling.

Contextual Bandit Optimization with Pre-Trained Neural Networks Neural Thompson Sampling

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T21:34:10.717235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.