Paper Citation Record · LEDGER

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2510.12152.

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

pith.paper-citation-record.v1
2510.12152 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:08:35.693456Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 8b2e1561-a487-42fe-8183-20b87ccfeffe · outbound

This paper cites write newline.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.583366Z

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Observation e541812b-f769-46d6-a82b-6567182fc513 · outbound

This paper cites Fighting bandits with a new kind of smoothness.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Fighting bandits with a new kind of smoothness

Reference 2

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Observation 2bbb536a-e2d6-465c-9cd5-b138d073dab4 · outbound

This paper cites The nonstochastic multiarmed bandit problem.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality The nonstochastic multiarmed bandit problem

Reference 3

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Observation 282e5612-477d-4c2c-becd-fe889bd7bdcc · outbound

This paper cites Decoupling exploration and exploitation in multi-armed bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Decoupling exploration and exploitation in multi-armed bandits

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.743170Z

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Observation 0a1c484e-1ea1-4d6a-8c8b-3ccff044cfa8 · outbound

This paper cites Bandit algorithms for e-commerce recommender systems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Bandit algorithms for e-commerce recommender systems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.795159Z

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Observation 3e0a3ebc-41ab-41c3-80fd-281afc2f3ee6 · outbound

This paper cites Five miracles of mirror descent, 2019.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Five miracles of mirror descent, 2019

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.849827Z

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Observation 4fdd0aa5-dc12-4b4f-ae22-9dcab4e02cb8 · outbound

This paper cites Regret analysis of stochastic and nonstochastic multi-armed bandit problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Regret analysis of stochastic and nonstochastic multi-armed bandit problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.905844Z

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Observation 81d367f8-68fd-4099-9e69-9abc30cd7ac7 · outbound

This paper cites The best of both worlds: Stochastic and adversarial bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality The best of both worlds: Stochastic and adversarial bandits

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:31.963263Z

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Observation 5ae193ea-4ef8-4932-b117-789e3bbff22f · outbound

This paper cites A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.022880Z

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Observation 1e14f41d-a43b-4a9e-b494-370aea945480 · outbound

This paper cites Optimization of Epsilon-Greedy Exploration.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Optimization of Epsilon-Greedy Exploration

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.141050Z

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Observation 7c56f155-a18f-4415-8e7c-30976d4ad614 · outbound

This paper cites Geometric resampling in nearly linear time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds guarantee in bandit problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Geometric resampling in nearly linear time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds guarantee in bandit problems

Reference 11

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Observation 4070d39e-dd31-4c0b-98d3-91e5ac927278 · outbound

This paper cites Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.492703Z

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Observation af83966e-fafb-4790-9e95-e9f2e46b9546 · outbound

This paper cites On explore-then-commit strategies.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality On explore-then-commit strategies

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.653283Z

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Observation 85ee46ac-06be-48dc-bd0d-43cc2ac8459f · outbound

This paper cites Follow-the-Perturbed-Leader achieves Best-of-Both-Worlds for bandit problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Follow-the-Perturbed-Leader achieves Best-of-Both-Worlds for bandit problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.739844Z

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Observation fd173f7b-364c-4ac8-a834-fd5652efcdc7 · outbound

This paper cites Adaptive learning rate for Follow-the-Regularized-Leader : Competitive analysis and Best-of-Both-Worlds.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Adaptive learning rate for Follow-the-Regularized-Leader : Competitive analysis and Best-of-Both-Worlds

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:32.908261Z

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Observation 7aba412f-9013-4fcb-a435-a88ec9fa3015 · outbound

This paper cites Improved Best-of-Both-Worlds guarantees for multi-armed bandits: FTRL with general regularizers and multiple optimal arms.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Improved Best-of-Both-Worlds guarantees for multi-armed bandits: FTRL with general regularizers and multiple optimal arms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.089293Z

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Observation f5e91056-a704-4e3a-9107-d03a4d61c295 · outbound

This paper cites An -best-arm identification algorithm for fixed-confidence and beyond.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality An -best-arm identification algorithm for fixed-confidence and beyond

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.227646Z

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Observation 8105a29d-78af-4284-82c0-304c68afeef9 · outbound

This paper cites On the optimality of perturbations in stochastic and adversarial multi-armed bandit problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality On the optimality of perturbations in stochastic and adversarial multi-armed bandit problems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.406411Z

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Observation 593e55c9-e3be-4e5e-b0f2-b73fa619449a · outbound

This paper cites Follow-the-Perturbed-Leader with F r\'echet-type tail distributions: Optimality in adversarial bandits and best-of-both-worlds.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Follow-the-Perturbed-Leader with F r\'echet-type tail distributions: Optimality in adversarial bandits and best-of-both-worlds

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.598175Z

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Observation 6d8b16d7-d1ef-4672-800b-4784a17020b0 · outbound

This paper cites Revisiting Follow-the-Perturbed-Leader with unbounded perturbations in bandit problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Revisiting Follow-the-Perturbed-Leader with unbounded perturbations in bandit problems

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.738612Z

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Observation cb2659cc-c4ab-433f-83b5-93d7c7cc5b92 · outbound

This paper cites Schwartz, and Jacob Abernethy.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Schwartz, and Jacob Abernethy

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.844790Z

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Observation 7f873626-0803-4699-85e1-e466c3959a7b · outbound

This paper cites Low-rank bandit methods for high-dimensional dynamic pricing.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Low-rank bandit methods for high-dimensional dynamic pricing

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:33.987126Z

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Observation e94a06b5-e559-4839-8d97-622e132d28c7 · outbound

This paper cites Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:34.050481Z

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Observation d1f195b8-ab4e-4d32-9f27-6f3251520ba6 · outbound

This paper cites Conic optimization via operator splitting and homogeneous self-dual embedding.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Conic optimization via operator splitting and homogeneous self-dual embedding

Reference 24

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Observation 0a40b282-1cff-46b3-916c-130cee3ceb22 · outbound

This paper cites NIST handbook of mathematical functions hardback and CD-ROM.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality NIST handbook of mathematical functions hardback and CD-ROM

Reference 25

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Observation 0a5b5b7f-19be-438d-bb23-1fbbea30172c · outbound

This paper cites Tsallis-inf for decoupled exploration and exploitation in multi-armed bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Tsallis-inf for decoupled exploration and exploitation in multi-armed bandits

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:34.262254Z

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Observation 48528644-b780-412c-b2c6-e81a547e0dfc · outbound

This paper cites Follow the perturbed leader: Optimism and fast parallel algorithms for smooth minimax games.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Follow the perturbed leader: Optimism and fast parallel algorithms for smooth minimax games

Reference 27

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Observation e7a8c603-6efc-40d6-b8a7-ead6231ae691 · outbound

This paper cites Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:34.538073Z

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Observation 5ae7962a-492c-490c-937d-5eaa3135ef06 · outbound

This paper cites More adaptive algorithms for adversarial bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality More adaptive algorithms for adversarial bandits

Reference 29

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Observation cfc70a12-31a9-4877-8d0f-23c7a67d0df1 · outbound

This paper cites Follow-the-Perturbed-Leader Approaches Best-of-Both-Worlds for the m-Set Semi-Bandit Problems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Follow-the-Perturbed-Leader Approaches Best-of-Both-Worlds for the m-Set Semi-Bandit Problems

Reference 30

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Observation 50616895-bab8-4fd3-b83a-27cab1436c84 · outbound

This paper cites Sim-to-real transfer in deep reinforcement learning for robotics: a survey.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Sim-to-real transfer in deep reinforcement learning for robotics: a survey

Reference 31

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Observation e8105c46-a957-420f-97b5-dd5e03d762e6 · outbound

This paper cites Large-scale bandit approaches for recommender systems.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Large-scale bandit approaches for recommender systems

Reference 32

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Observation 81832b05-4320-4fe2-b84c-5163929192d9 · outbound

This paper cites Tsallis- INF : A n optimal algorithm for stochastic and adversarial bandits.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Tsallis- INF : A n optimal algorithm for stochastic and adversarial bandits

Reference 33

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Observation e3ed60d5-f6f9-42a4-9ce7-df3f400b1b0a · outbound

This paper cites @esa (Ref.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality @esa (Ref

Reference 34

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Observation eb280f17-edba-4edc-b93a-1f3917e301bb · outbound

This paper cites an unresolved cited work.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality Unresolved cited work

Reference 35

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Observation af35d759-7b1c-4c33-a4f4-7434c274fe1c · outbound

This paper cites A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds.

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T10:08:35.693456Z

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Pith citing papers

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