Paper Citation Record · LEDGER

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem

As of 11 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2603.10184.

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

pith.paper-citation-record.v1
2603.10184 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T23:50:03.797434Z

measured 18 of 18 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

18 of 18 outbound references displayed

verified exact3
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc11ff56-fda4-4244-a4db-95f5ac67a88a · outbound

This paper cites an unresolved cited work.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Unresolved cited work

Reference 1

Resolution: verified exact; doi, observed 2026-07-14T23:50:51.177289Z

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 8ef37035-40d9-4e71-a94b-694c3ceded4d · outbound

This paper cites Accurate Inference for Adaptive Linear Models.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Accurate Inference for Adaptive Linear Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cacbf0bb-d771-4e8e-8e4b-95383e6e019e · outbound

This paper cites Confidence Intervals for Policy Evaluation in Adaptive Experiments.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Confidence Intervals for Policy Evaluation in Adaptive Experiments

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2504e8c7-052c-41e4-9da3-3bd0125f0378 · outbound

This paper cites Qiyang Han, Koulik Khamaru, and Cun-Hui Zhang.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Qiyang Han, Koulik Khamaru, and Cun-Hui Zhang

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1945422e-42a8-474d-8594-ad4d31bca0c4 · outbound

This paper cites UCB algorithms for multi-armed bandits: Precise regret and adaptive inference.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem UCB algorithms for multi-armed bandits: Precise regret and adaptive inference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dae82226-96a1-4bce-a220-f05c97fa1d82 · outbound

This paper cites Adversarial Attacks on Stochastic Bandits.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Adversarial Attacks on Stochastic Bandits

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e5ba997-a62e-428c-b28d-e3e8976aab0c · outbound

This paper cites Inference with the Upper Confidence Bound Algorithm.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Inference with the Upper Confidence Bound Algorithm

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0c86648-58e7-421b-905b-000fc1c0f08d · outbound

This paper cites T.L Lai and Herbert Robbins.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem T.L Lai and Herbert Robbins

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7c7fb78-0810-446e-9c37-9db25768596b · outbound

This paper cites doi: https://doi.org/10.1016/0196-8858(85) 90002-8.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem doi: https://doi.org/10.1016/0196-8858(85) 90002-8

Reference 9

Resolution: verified exact; doi, observed 2026-07-14T23:50:51.166422Z

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 5a99c6c7-5982-4f3b-8597-24277d516007 · outbound

This paper cites 12 ThodorisLykouris, VahabMirrokni, andRenatoPaesLeme.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem 12 ThodorisLykouris, VahabMirrokni, andRenatoPaesLeme

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c119f4f0-b9a2-4621-9b98-d057dea8c80f · outbound

This paper cites Stochastic bandits robust to adversarial corruptions.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Stochastic bandits robust to adversarial corruptions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a6aa7c-50b5-4dc6-bd2c-19b4e6e664a9 · outbound

This paper cites an unresolved cited work.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a4aa69b-83fa-4328-a11c-cb2a55561fe2 · outbound

This paper cites an unresolved cited work.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31faa351-fdfa-43e3-9055-165091e39d61 · outbound

This paper cites an unresolved cited work.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Unresolved cited work

Reference 14

Resolution: verified exact; doi, observed 2026-07-14T23:50:51.165747Z

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 0d0e9bbb-cf3d-420e-8299-68743834bf26 · outbound

This paper cites Estimating means of bounded random variables by betting.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Estimating means of bounded random variables by betting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff05950c-ff43-4c8b-a250-d27aee966f4a · outbound

This paper cites Inference for Batched Bandits.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Inference for Batched Bandits

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5ae8544-b627-433f-ac23-464873e367e2 · outbound

This paper cites KX i=1 bℓ 2 t,i xt,i # =E.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem KX i=1 bℓ 2 t,i xt,i # =E

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be1e4ea1-9823-473c-a6fc-6f4066de28d9 · outbound

This paper cites Observe that for the original rewardsℓt, by Lai and Wei (1982), it holds that, 1√na,T TX t=1 (ℓt −µ a)1{A t =a} D− → N 0, σ2 a ,∀a∈ {1,2,.

Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem Observe that for the original rewardsℓt, by Lai and Wei (1982), it holds that, 1√na,T TX t=1 (ℓt −µ a)1{A t =a} D− → N 0, σ2 a ,∀a∈ {1,2,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T23:50:03.797434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.