Paper Citation Record · LEDGER

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2506.06521.

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

pith.paper-citation-record.v1
2506.06521 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:09:21.939768Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:10:52.644951Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T06:45:40.553981Z

Reference resolution

57 of 57 outbound references displayed

verified exact3
verified fuzzy38
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 752a21c9-eded-434d-be7b-0f168f5d613f · outbound

This paper cites Navigating to the best policy in markov decision processes.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Navigating to the best policy in markov decision processes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:23.062037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e9af9e9-27e2-47d8-8e8b-e7c0ff6e7545 · outbound

This paper cites Logarithmic online regret bounds for undiscounted reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Logarithmic online regret bounds for undiscounted reinforcement learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.650637Z

Source-reported events for the cited work

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Observation 2f7ec633-71fd-414e-bb52-31eb330fa6ce · outbound

This paper cites Finite-time analysis of the multiarmed bandit problem.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Finite-time analysis of the multiarmed bandit problem

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:23.022547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1560998c-4618-4634-a281-39ba3a04691c · outbound

This paper cites Near-optimal regret bounds for reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Near-optimal regret bounds for reinforcement learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.660747Z

Source-reported events for the cited work

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Observation e5655449-346d-46ee-9f2c-2fe69a778fac · outbound

This paper cites Minimax regret bounds for reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Minimax regret bounds for reinforcement learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.665444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54fec24-9f68-4cf5-aa49-5f8fe58a873c · outbound

This paper cites REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.670031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c29630a4-0f78-40e9-887c-b334b8b794a4 · outbound

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

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Regret analysis of stochastic and nonstochastic multi-armed bandit problems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.675666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f3663fe-4d78-4094-b5ae-d7cf90cf1a0d · outbound

This paper cites Top-k off-policy correction for a reinforce recommender system.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Top-k off-policy correction for a reinforce recommender system

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.961449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1ac95b1-9f22-4f96-ae91-10a8d6604ec1 · outbound

This paper cites Variance-Aware Sparse Linear Bandits.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Variance-Aware Sparse Linear Bandits

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:09:22.085200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cec2c58e-e241-40a0-9c94-fdd2d08dfd2b · outbound

This paper cites Policy certificates: Towards accountable reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Policy certificates: Towards accountable reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.912679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6fa8dfe2-4779-4705-9abb-c97da5ade988 · outbound

This paper cites Beyond value-function gaps: Improved instance-dependent regret bounds for episodic reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Beyond value-function gaps: Improved instance-dependent regret bounds for episodic reinforcement learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.695323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b87a59-dc4d-49ed-a24e-ebb846250806 · outbound

This paper cites Gap-dependent bounds for two-player markov games.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Gap-dependent bounds for two-player markov games

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.865549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a168d2be-fb72-4c2d-a587-6dbfc37b7b0a · outbound

This paper cites Cascaded gaps: Towards logarithmic regret for risk-sensitive reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Cascaded gaps: Towards logarithmic regret for risk-sensitive reinforcement learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.842175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d22e8a7b-3d5e-480c-8a43-fe6b483cb5ad · outbound

This paper cites Efficient bias-span-constrained exploration-exploitation in reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Efficient bias-span-constrained exploration-exploitation in reinforcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.820123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec4381ac-2eb4-48cf-9940-8968b0f5725d · outbound

This paper cites Logarithmic regret for reinforcement learning with linear function approximation.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Logarithmic regret for reinforcement learning with linear function approximation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.800170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0076f097-b362-4663-b39d-45d547528fa1 · outbound

This paper cites Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent regret bounds.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent regret bounds

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.780075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4c146e2b-e269-42fb-9c88-5f62ed713de8 · outbound

This paper cites Is q-learning provably efficient? Advances in neural information processing systems, 31, 2018.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Is q-learning provably efficient? Advances in neural information processing systems, 31, 2018

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.723948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf290a2-8bd0-4d6a-927c-49934c46ac58 · outbound

This paper cites Reward-free exploration for reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Reward-free exploration for reinforcement learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.751095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4001edbd-21f0-4c19-b55c-fc39d77c45e5 · outbound

This paper cites Planning in markov decision processes with gap-dependent sample complexity.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Planning in markov decision processes with gap-dependent sample complexity

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.729062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e9893598-862c-4221-92c3-14e79de722bd · outbound

This paper cites Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.708304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e3b9b6b2-9766-4c14-9ef0-847e3dce72d3 · outbound

This paper cites Asymptotically efficient adaptive allocation rules.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Asymptotically efficient adaptive allocation rules

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.742030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e475c0b6-34e8-4d90-b0ca-53a9292c24d2 · outbound

This paper cites Breaking the sample complexity barrier to regret-optimal model-free reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Breaking the sample complexity barrier to regret-optimal model-free reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.675579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0374572d-fe69-4408-9c01-0759e382bb4f · outbound

This paper cites Continuous control with deep reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Continuous control with deep reinforcement learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.751752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a8208e-7d51-4449-b65d-cd43cbc729b9 · outbound

This paper cites Deep reinforcement learning for dynamic treatment regimes on medical registry data.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Deep reinforcement learning for dynamic treatment regimes on medical registry data

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.655567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dfa9340b-fa18-408b-bcbb-1d0b634b2f37 · outbound

This paper cites Adaptive Sampling for Best Policy Identification in Markov Decision Processes.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Adaptive Sampling for Best Policy Identification in Markov Decision Processes

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:09:22.039979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 82315765-0338-43ad-bf3a-22ed3b6f355b · outbound

This paper cites Empirical Bernstein Bounds and Sample Variance Penalization.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Empirical Bernstein Bounds and Sample Variance Penalization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.766080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da871a9a-82bc-4c25-9641-0600ed1a9997 · outbound

This paper cites Ucb momentum q-learning: Correcting the bias without forgetting.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Ucb momentum q-learning: Correcting the bias without forgetting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.634394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7e4070e2-adca-46e5-9b69-c550d4538112 · outbound

This paper cites Reinforcement learning for optimized trade execution.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Reinforcement learning for optimized trade execution

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.612325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 840d6741-43ff-467c-9513-0fb223349c7e · outbound

This paper cites On instance-dependent bounds for offline reinforcement learning with linear function approximation.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs On instance-dependent bounds for offline reinforcement learning with linear function approximation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.592062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83035fc7-3068-41b5-9a09-6380cec19577 · outbound

This paper cites Exploration in structured reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Exploration in structured reinforcement learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.572522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 143457cc-037a-4666-9f5a-426af5f8d651 · outbound

This paper cites Why is posterior sampling better than optimism for reinforcement learning? In International conference on machine learning, pages 2701--2710.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Why is posterior sampling better than optimism for reinforcement learning? In International conference on machine learning, pages 2701--2710

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.552330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc82e5fe-f4e1-49a8-9b08-93ef8f346b0c · outbound

This paper cites Reinforcement learning in linear mdps: Constant regret and representation selection.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Reinforcement learning in linear mdps: Constant regret and representation selection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.530992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fdcbba15-75cf-4a2c-8f4e-d664249465b1 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Mastering the game of go with deep neural networks and tree search

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.797839Z

Source-reported events for the cited work

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Observation 1c0afb3a-986d-4ccd-8bb4-e9c44f172cfb · outbound

This paper cites Non-asymptotic gap-dependent regret bounds for tabular mdps.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Non-asymptotic gap-dependent regret bounds for tabular mdps

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.802739Z

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Observation 53f03b79-cce4-4b1a-a750-b79a831414fd · outbound

This paper cites Reinforcement learning: An introduction, volume 1.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Reinforcement learning: An introduction, volume 1

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.806964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9bbdc6-dc6b-45b5-b181-dd2192b8093f · outbound

This paper cites Variance-aware regret bounds for undiscounted reinforcement learning in mdps.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Variance-aware regret bounds for undiscounted reinforcement learning in mdps

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.481576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a7757793-65e0-4df2-a5bc-6aca707c4cd1 · outbound

This paper cites Optimistic linear programming gives logarithmic regret for irreducible mdps.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Optimistic linear programming gives logarithmic regret for irreducible mdps

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.460484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 87b74cce-14e2-47e2-a05b-8814cb258c4c · outbound

This paper cites Near instance-optimal pac reinforcement learning for deterministic mdps.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Near instance-optimal pac reinforcement learning for deterministic mdps

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.441791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 646c21e6-60f6-4282-ba5a-dbe02e9614a8 · outbound

This paper cites Optimistic pac reinforcement learning: the instance-dependent view.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Optimistic pac reinforcement learning: the instance-dependent view

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.422810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 665fbd0f-d338-43cc-89fe-2f62a84c2986 · outbound

This paper cites Reinforcement learning with logarithmic regret and policy switches.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Reinforcement learning with logarithmic regret and policy switches

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.403477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a4fba1ab-3745-4ef1-9a60-f3f745eabfc8 · outbound

This paper cites Instance-dependent near-optimal policy identification in linear mdps via online experiment design.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Instance-dependent near-optimal policy identification in linear mdps via online experiment design

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.387794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c96a5531-24de-4071-ac6b-bb1c7376ee50 · outbound

This paper cites First-order regret in reinforcement learning with linear function approximation: A robust estimation approach.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs First-order regret in reinforcement learning with linear function approximation: A robust estimation approach

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.371017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 343a71d3-ab56-4d4c-a37e-13f8cab7b505 · outbound

This paper cites Beyond no regret: Instance-dependent pac reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Beyond no regret: Instance-dependent pac reinforcement learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.352868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 587e52c9-d96d-4887-80a9-2f0250ffdba0 · outbound

This paper cites On gap-dependent bounds for offline reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs On gap-dependent bounds for offline reinforcement learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.335760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c2295a7-13c1-4934-a9f2-7106bdeb9912 · outbound

This paper cites Near-optimal randomized exploration for tabular markov decision processes.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Near-optimal randomized exploration for tabular markov decision processes

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.318255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee3fccd1-e11e-4db4-9784-a506b0ca06b9 · outbound

This paper cites Fine-grained gap-dependent bounds for tabular mdps via adaptive multi-step bootstrap.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Fine-grained gap-dependent bounds for tabular mdps via adaptive multi-step bootstrap

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.300451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c561d791-c61e-41ee-922e-0c6d2e04dd84 · outbound

This paper cites Q-learning with logarithmic regret.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Q-learning with logarithmic regret

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.279268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e09e3872-2afb-4001-84da-90d806ce96b6 · outbound

This paper cites Tighter problem-dependent regret bounds in reinforcement learning without domain knowledge using value function bounds.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Tighter problem-dependent regret bounds in reinforcement learning without domain knowledge using value function bounds

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.875678Z

Source-reported events for the cited work

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Observation 1fdf739b-4ba0-4d61-acb9-a387c1a54368 · outbound

This paper cites Regret minimization for reinforcement learning by evaluating the optimal bias function.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Regret minimization for reinforcement learning by evaluating the optimal bias function

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.246652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03367650-80df-4da6-97d3-4005db1289cb · outbound

This paper cites Almost optimal model-free reinforcement learningvia reference-advantage decomposition.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Almost optimal model-free reinforcement learningvia reference-advantage decomposition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.224318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a62ee762-7be0-4634-a9f7-806d703c4041 · outbound

This paper cites Is reinforcement learning more difficult than bandits? a near-optimal algorithm escaping the curse of horizon.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Is reinforcement learning more difficult than bandits? a near-optimal algorithm escaping the curse of horizon

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.203614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation eb10c7a5-ddd7-49d9-9810-6017f98d517a · outbound

This paper cites Improved variance-aware confidence sets for linear bandits and linear mixture mdp.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Improved variance-aware confidence sets for linear bandits and linear mixture mdp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.184382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 437ff0ee-9e1e-4079-b4c7-1dd3ac6a4efa · outbound

This paper cites Horizon-free reinforcement learning in polynomial time: the power of stationary policies.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Horizon-free reinforcement learning in polynomial time: the power of stationary policies

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.165150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 744f8b4a-6ecc-44b7-bd4a-b4cd2893f646 · outbound

This paper cites Settling the sample complexity of online reinforcement learning.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Settling the sample complexity of online reinforcement learning

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.917081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1f3bd36-1d48-4a40-8458-ac15aa3701e9 · outbound

This paper cites Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:09:21.992642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a74689ed-21ec-46a7-a988-4e5560f21227 · outbound

This paper cites Nearly minimax optimal reinforcement learning for linear mixture markov decision processes.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Nearly minimax optimal reinforcement learning for linear mixture markov decision processes

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:22.133022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46abb972-bf09-441b-a206-dd59d2d18436 · outbound

This paper cites Sharp variance-dependent bounds in reinforcement learning: Best of both worlds in stochastic and deterministic environments.

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs Sharp variance-dependent bounds in reinforcement learning: Best of both worlds in stochastic and deterministic environments

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T06:09:21.939768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 229aedfe-0a81-41e4-9f90-efbe3fc4d041 · inbound

On-line Learning in Tree MDPs by Treating Policies as Bandit Arms cites this paper.

On-line Learning in Tree MDPs by Treating Policies as Bandit Arms Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:45:40.556056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.