Paper Citation Record · LEDGER

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2411.10906.

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

pith.paper-citation-record.v1
2411.10906 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:18:52.722594Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy25
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1398c259-6c1b-47a2-8ff9-9a44ff679c91 · outbound

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

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Near-optimal regret bounds for reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.392987Z

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Observation a6b2ad8b-b71b-4e4b-9143-12d427be329c · outbound

This paper cites Modular multitask reinforcement learning with policy sketches.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Modular multitask reinforcement learning with policy sketches

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.374662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b19f5de2-0bf4-413c-accf-cef2344cd4a5 · outbound

This paper cites Litvak, Alain Pajor, and Nicole Tomczak-Jaegermann.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Litvak, Alain Pajor, and Nicole Tomczak-Jaegermann

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.357070Z

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Observation bf0b6466-2172-4009-8e4e-b2eb003ca2e0 · outbound

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

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Logarithmic online regret bounds for undiscounted reinforcement learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.340239Z

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Observation e1ca11dc-8f20-4c53-8c7b-fe88ab9a6e1e · outbound

This paper cites Bellemare, Joel Veness, and Michael Bowling.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Bellemare, Joel Veness, and Michael Bowling

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.321736Z

Source-reported events for the cited work

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Observation 97a11bf9-ea1d-41d7-ab13-f446d8389c64 · outbound

This paper cites Vallis, Bruno Lacerda, and Nick Hawes.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Vallis, Bruno Lacerda, and Nick Hawes

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.302275Z

Source-reported events for the cited work

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Observation d3c4d07f-28e5-4dea-8ca1-9d678e81d4e3 · outbound

This paper cites Towards deployment-efficient reinforcement learning: Lower bound and optimality.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Towards deployment-efficient reinforcement learning: Lower bound and optimality

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.283165Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 372f5dd2-d667-4c9a-aed9-75d1857ce6d9 · outbound

This paper cites Model-based reinforcement learning with multinomial logistic function approximation.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Model-based reinforcement learning with multinomial logistic function approximation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.262635Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6552027-5548-461a-9955-079db7117c44 · outbound

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

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Nearly minimax optimal reinforcement learning for linear markov decision processes

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.245058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9bdbc462-3502-4ced-a7b9-6cc5e73d21a9 · outbound

This paper cites an unresolved cited work.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:18:53.221892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f82889a5-f102-4f89-9389-c60329763d65 · outbound

This paper cites Sample-efficient reinforcement learning is feasible for linearly realizable mdps with limited revisiting.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Sample-efficient reinforcement learning is feasible for linearly realizable mdps with limited revisiting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.202779Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 62607fc6-5394-4e64-bfc2-4c11e4c763b9 · outbound

This paper cites Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.183796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 363e5c45-ff6d-4d6b-84a0-5c2db4407193 · outbound

This paper cites Bandit Algorithms , 2020.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Bandit Algorithms , 2020

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.164415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation edf825ab-7dd8-48cd-9259-1bc7e99ec9f3 · outbound

This paper cites Asynchronous Methods for Deep Reinforcement Learning.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Asynchronous Methods for Deep Reinforcement Learning

Reference 14

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Source-reported events for the cited work

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Observation 3821c233-1aad-46e0-bef1-2349c716c40f · outbound

This paper cites Machado, Marc G.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Machado, Marc G

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.145694Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 32e0c405-9279-463b-ab75-872609f8c0fc · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Playing Atari with Deep Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:52.625740Z

Source-reported events for the cited work

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Observation d3e5d9aa-7d43-4023-9463-b9eaa3702465 · outbound

This paper cites Human-level control through deep reinforcement learning.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Human-level control through deep reinforcement learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:52.631053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b45d5a89-f0e8-47a8-a0c9-41a8f31d7b08 · outbound

This paper cites Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:18:52.825722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 24aae1bb-eed8-4143-8c25-ede472d695c1 · outbound

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

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Reinforcement learning in linear mdps: Constant regret and representation selection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.112624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83d266a6-519a-4f58-8dbe-b371f0cacf06 · outbound

This paper cites Markov decision processes: discrete stochastic dynamic programming, 2014.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Markov decision processes: discrete stochastic dynamic programming, 2014

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.093425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c93c46c4-b60c-442f-ab8f-d8a4a63c2ca0 · outbound

This paper cites Mastering the game of go without human knowledge.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Mastering the game of go without human knowledge

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.073031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c47c58c6-9b23-4118-9462-ebd840b1094e · outbound

This paper cites Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:18:52.794687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d6d8bb08-1128-40a9-a6f7-f0c9672a9860 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Proximal Policy Optimization Algorithms

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:52.665619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbcda732-6ebe-47c8-b30b-a4ea4e5ee5eb · outbound

This paper cites Sketching for first order method: Efficient algorithm for low-bandwidth channel and vulnerability.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Sketching for first order method: Efficient algorithm for low-bandwidth channel and vulnerability

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.054990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ab10977e-602d-4db6-9050-5247ad61381b · outbound

This paper cites Sketching linear classifiers over data streams.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Sketching linear classifiers over data streams

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.036963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2717ec9d-87f1-4481-8d9b-643746ffa28a · outbound

This paper cites How close is the sample covariance matrix to the actual covariance matrix?, 2010.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs How close is the sample covariance matrix to the actual covariance matrix?, 2010

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:53.018862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9dfbbbb-9606-49cd-b601-3f8bd5a893f5 · outbound

This paper cites Wagenmaker, Yifang Chen, Max Simchowitz, Simon S.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Wagenmaker, Yifang Chen, Max Simchowitz, Simon S

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.999857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc067ae4-1ac6-4bc0-890f-d8d15c9095c4 · outbound

This paper cites Woodruff.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Woodruff

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T19:18:52.693929Z

Source-reported events for the cited work

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Observation c4134514-bf53-49fa-abf3-c6881c820739 · outbound

This paper cites Provably efficient reinforcement learning with linear function approximation under adaptivity constraints.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Provably efficient reinforcement learning with linear function approximation under adaptivity constraints

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.967458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4c7a4f59-bac1-4d04-95b2-77bf3ea2cd6c · outbound

This paper cites A general framework for sequential decision-making under adaptivity constraints.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs A general framework for sequential decision-making under adaptivity constraints

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.949510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd6ca5f4-986f-4186-9216-2d7eba72a948 · outbound

This paper cites Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.930717Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ec2eaef-735e-4c4c-8eae-2c4b6c9f5d15 · outbound

This paper cites Varshney, and Ashish Jagmohan.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Varshney, and Ashish Jagmohan

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.911774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17ccee87-b0e9-4823-b61d-0af3101ee28a · outbound

This paper cites Making linear mdps practical via contrastive representation learning.

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs Making linear mdps practical via contrastive representation learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:18:52.892212Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.