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Paper Citation Record · LEDGER

Linear Mixture Distributionally Robust Markov Decision Processes

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2505.18044.

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

pith.paper-citation-record.v1
2505.18044 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:44:16.530086Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

51 of 51 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dc186288-6cb8-4ced-95f5-554b82e2a0c9 · outbound

This paper cites Improved algorithms for linear stochastic bandits.Advances in Neural Information Processing Systems, 24, 2011.

Linear Mixture Distributionally Robust Markov Decision Processes Improved algorithms for linear stochastic bandits.Advances in Neural Information Processing Systems, 24, 2011

Reference 1

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Observation bb835027-f4f9-4f05-93ca-afff8eca1798 · outbound

This paper cites Model-based rein- forcement learning with value-targeted regression.

Linear Mixture Distributionally Robust Markov Decision Processes Model-based rein- forcement learning with value-targeted regression

Reference 2

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Observation 38a13ecd-1e1d-4364-8365-9ae7148b5933 · outbound

This paper cites Robust reinforcement learning using least squares policy iteration with provable performance guarantees.

Linear Mixture Distributionally Robust Markov Decision Processes Robust reinforcement learning using least squares policy iteration with provable performance guarantees

Reference 3

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Observation 00956383-db3a-4030-bb26-9b74abc7e6f1 · outbound

This paper cites an unresolved cited work.

Linear Mixture Distributionally Robust Markov Decision Processes Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1dd62c58-047c-4ffc-8b5b-baf919b0cf50 · outbound

This paper cites Provably efficient exploration in policy optimization.

Linear Mixture Distributionally Robust Markov Decision Processes Provably efficient exploration in policy optimization

Reference 5

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 3f20851a-ba71-43a7-af1d-095e670e23a8 · outbound

This paper cites A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models.

Linear Mixture Distributionally Robust Markov Decision Processes A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 6

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Source-reported events for the cited work

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Observation 19a16ae0-0857-41d3-ae1b-e2da5c47ff67 · outbound

This paper cites Off-dynamics reinforcement learning: Training for transfer with domain classifiers.

Linear Mixture Distributionally Robust Markov Decision Processes Off-dynamics reinforcement learning: Training for transfer with domain classifiers

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 54623bc2-edaa-4cb8-993d-1db9562a20cc · outbound

This paper cites Birkhauser Boston Inc., 1989.

Linear Mixture Distributionally Robust Markov Decision Processes Birkhauser Boston Inc., 1989

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 7ab5c91c-f782-4b02-9065-0bc0a743a499 · outbound

This paper cites Robust markov decision processes: Beyond rectangu- larity.Mathematics of Operations Research, 48(1):203–226, 2023.

Linear Mixture Distributionally Robust Markov Decision Processes Robust markov decision processes: Beyond rectangu- larity.Mathematics of Operations Research, 48(1):203–226, 2023

Reference 9

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 3fa3af11-b595-4805-931b-4d939d270587 · outbound

This paper cites Off-dynamics reinforcement learning via domain adaptation and reward augmented imitation.

Linear Mixture Distributionally Robust Markov Decision Processes Off-dynamics reinforcement learning via domain adaptation and reward augmented imitation

Reference 10

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Observation 8cf0cc27-d3e2-42cc-94fc-00e9988764f3 · outbound

This paper cites Kullback-leibler divergence constrained distributionally robust optimization.Available at Optimization Online, 1(2):9, 2013.

Linear Mixture Distributionally Robust Markov Decision Processes Kullback-leibler divergence constrained distributionally robust optimization.Available at Optimization Online, 1(2):9, 2013

Reference 11

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Observation 26f5d310-fae8-4dec-9df9-918a18143f24 · outbound

This paper cites Robust dynamic programming.Mathematics of Operations Research, 30(2): 257–280, 2005.

Linear Mixture Distributionally Robust Markov Decision Processes Robust dynamic programming.Mathematics of Operations Research, 30(2): 257–280, 2005

Reference 12

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Observation 1133a7aa-aac7-4269-9920-365395c561b4 · outbound

This paper cites Model-based reinforcement learning with value-targeted regression.

Linear Mixture Distributionally Robust Markov Decision Processes Model-based reinforcement learning with value-targeted regression

Reference 13

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Observation 9681b5ac-3886-4ad9-a7e8-705f2973edb4 · outbound

This paper cites Reinforcement learning in robotics: A survey.

Linear Mixture Distributionally Robust Markov Decision Processes Reinforcement learning in robotics: A survey

Reference 14

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation a8f9b543-73ed-47b2-be81-feb4db80c716 · outbound

This paper cites The transferability approach: Crossing the reality gap in evolutionary robotics.IEEE Transactions on Evolutionary Computa- tion, 17(1):122–145, 2012.

Linear Mixture Distributionally Robust Markov Decision Processes The transferability approach: Crossing the reality gap in evolutionary robotics.IEEE Transactions on Evolutionary Computa- tion, 17(1):122–145, 2012

Reference 15

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Observation 537e3031-7b53-4ac4-84f6-ac234e66cc16 · outbound

This paper cites Improved algorithm for adversarial linear mixture mdps with bandit feedback and unknown transition.

Linear Mixture Distributionally Robust Markov Decision Processes Improved algorithm for adversarial linear mixture mdps with bandit feedback and unknown transition

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9e4b0ec0-dad0-4e2b-8215-312c05ec1e7f · outbound

This paper cites Policy gradient algorithms for robust mdps with non-rectangular uncertainty sets.arXiv preprint arXiv:2305.19004, 2023.

Linear Mixture Distributionally Robust Markov Decision Processes Policy gradient algorithms for robust mdps with non-rectangular uncertainty sets.arXiv preprint arXiv:2305.19004, 2023

Reference 17

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Observation c0a9d7d5-2e5b-490a-9ce1-c4a8d716093d · outbound

This paper cites Distributionally robust off-dynamics reinforcement learning: Prov- able efficiency with linear function approximation.

Linear Mixture Distributionally Robust Markov Decision Processes Distributionally robust off-dynamics reinforcement learning: Prov- able efficiency with linear function approximation

Reference 18

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Observation 0dc062d7-014f-4736-813b-841708978fd2 · outbound

This paper cites Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning.

Linear Mixture Distributionally Robust Markov Decision Processes Minimax optimal and computationally efficient algorithms for distributionally robust offline reinforcement learning

Reference 19

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 63daded1-ac5d-49ab-99aa-5b63af275ba0 · outbound

This paper cites Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning.

Linear Mixture Distributionally Robust Markov Decision Processes Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning

Reference 20

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Unavailable: canonical work link unavailable.

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Observation 95e6eb6c-acaa-4371-9d67-69ea426d175c · outbound

This paper cites Distributionally robust reinforcement learning with interactive data collection: Fundamental hardness and near-optimal algorithms.

Linear Mixture Distributionally Robust Markov Decision Processes Distributionally robust reinforcement learning with interactive data collection: Fundamental hardness and near-optimal algorithms

Reference 21

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Observation b85144d0-96dd-4589-b3c1-7a46c22f8c9f · outbound

This paper cites Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation.

Linear Mixture Distributionally Robust Markov Decision Processes Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation

Reference 22

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Observation 0c3ffa72-4d05-4171-ad13-ea9788ec4a94 · outbound

This paper cites Finite mixture models.Annual review of statistics and its application, 6(1):355–378, 2019.

Linear Mixture Distributionally Robust Markov Decision Processes Finite mixture models.Annual review of statistics and its application, 6(1):355–378, 2019

Reference 23

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Observation 44bb74d5-08e3-42dd-ae8a-af04cf923cc2 · outbound

This paper cites A simplex method for function minimization.The computer journal, 7(4):308–313, 1965.

Linear Mixture Distributionally Robust Markov Decision Processes A simplex method for function minimization.The computer journal, 7(4):308–313, 1965

Reference 24

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raw_fallback, observed 2026-08-07T14:44:23.223487Z

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Observation 67835c7f-4f71-4a3e-97e5-8b9fc42752ed · outbound

This paper cites Robust control of markov decision processes with uncertain transition matrices.Operations Research, 53(5):780–798, 2005.

Linear Mixture Distributionally Robust Markov Decision Processes Robust control of markov decision processes with uncertain transition matrices.Operations Research, 53(5):780–798, 2005

Reference 25

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Observation 839e7291-ab3a-497e-8e24-f86eb3b3790a · outbound

This paper cites Robustness in markov decision problems with uncertain transition matrices.Advances in Neural Information Processing Systems, 16, 2003.

Linear Mixture Distributionally Robust Markov Decision Processes Robustness in markov decision problems with uncertain transition matrices.Advances in Neural Information Processing Systems, 16, 2003

Reference 26

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Observation 18d66843-81be-44cc-aa87-48dd55c772f0 · outbound

This paper cites Assessing Generalization in Deep Reinforcement Learning.

Linear Mixture Distributionally Robust Markov Decision Processes Assessing Generalization in Deep Reinforcement Learning

Reference 27

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no resolver link, observed 2026-08-07T14:44:12.736776Z

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Unavailable: canonical work link unavailable.

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Observation 4142db4e-26e7-4179-b4b9-ce97031e44e5 · outbound

This paper cites Bridging distributionally robust learning and offline rl: An approach to mitigate distribution shift and partial data coverage.

Linear Mixture Distributionally Robust Markov Decision Processes Bridging distributionally robust learning and offline rl: An approach to mitigate distribution shift and partial data coverage

Reference 28

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e8fd1073-752e-49a0-92fe-9ccba380a9ab · outbound

This paper cites The infinite gaussian mixture model.Advances in Neural Information Processing Systems, 12, 1999.

Linear Mixture Distributionally Robust Markov Decision Processes The infinite gaussian mixture model.Advances in Neural Information Processing Systems, 12, 1999

Reference 29

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 45337095-b368-4d61-abe8-bec4486f2fed · outbound

This paper cites Gaussian mixture models.Encyclopedia of biometrics, 741(659-663): 3, 2009.

Linear Mixture Distributionally Robust Markov Decision Processes Gaussian mixture models.Encyclopedia of biometrics, 741(659-663): 3, 2009

Reference 30

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e64b59e7-8913-4b22-a6da-19a224eb9698 · outbound

This paper cites Markovian decision processes with uncertain transition probabilities.Operations Research, 21(3):728–740, 1973.

Linear Mixture Distributionally Robust Markov Decision Processes Markovian decision processes with uncertain transition probabilities.Operations Research, 21(3):728–740, 1973

Reference 31

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 942aebe0-2956-4962-aff9-008cd03e2f88 · outbound

This paper cites Distributionally robust model-based offline reinforcement learning with near-optimal sample complexity.Journal of Machine Learning Research, 25(200):1–91,.

Linear Mixture Distributionally Robust Markov Decision Processes Distributionally robust model-based offline reinforcement learning with near-optimal sample complexity.Journal of Machine Learning Research, 25(200):1–91,

Reference 32

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raw_fallback, observed 2026-08-07T14:44:22.019192Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation ac69338f-c324-45ef-b6b7-b78ecf2e27b3 · outbound

This paper cites The curious price of distributional robustness in reinforcement learning with a generative model.Advances in Neural Information Processing Systems, 36, 2024.

Linear Mixture Distributionally Robust Markov Decision Processes The curious price of distributional robustness in reinforcement learning with a generative model.Advances in Neural Information Processing Systems, 36, 2024

Reference 33

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1bc9241f-01ad-4e82-9451-126cbac39427 · outbound

This paper cites Robust offline reinforcement learning with linearly structuredf-divergence regularization.arXiv preprint arXiv:2411.18612, 2024.

Linear Mixture Distributionally Robust Markov Decision Processes Robust offline reinforcement learning with linearly structuredf-divergence regularization.arXiv preprint arXiv:2411.18612, 2024

Reference 34

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no resolver link, observed 2026-08-07T14:44:13.540609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:13.540609Z digest=sha256:386901c2b4884e0fcbdd0bdfa83e9f1d46cfe4959ed88809277a4e904fef365e

Observation b1cf2ff8-fe45-4251-b08c-791f50ab0925 · outbound

This paper cites Pessimistic model-based offline reinforcement learning under partial coverage.

Linear Mixture Distributionally Robust Markov Decision Processes Pessimistic model-based offline reinforcement learning under partial coverage

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:21.615475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:13.759936Z digest=sha256:4015e3cb980978519ba676a4fc34b09dff3759a2966ab857b4b9c7a29100b535

Observation 3e8d53b8-5bfb-4d31-816b-e06f9bef2976 · outbound

This paper cites Sample complexity of offline distributionally robust linear markov decision processes.

Linear Mixture Distributionally Robust Markov Decision Processes Sample complexity of offline distributionally robust linear markov decision processes

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:21.435252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:13.955582Z digest=sha256:929e72b35bd298c0f7dc133e86389e953bed3c305de2c865560876181558c036

Observation 48e2cbd0-62f7-4b33-aa0d-c0db5afc2550 · outbound

This paper cites Return augmented decision transformer for off-dynamics reinforcement learning.arXiv preprint arXiv:2410.23450, 2024.

Linear Mixture Distributionally Robust Markov Decision Processes Return augmented decision transformer for off-dynamics reinforcement learning.arXiv preprint arXiv:2410.23450, 2024

Reference 37

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unresolved
no resolver link, observed 2026-08-07T14:44:14.180938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:14.180938Z digest=sha256:80d98e17576aaa19f759efd85f714059ba985d7b1f0b408496d16d0d8fdaec36

Observation e85e83e1-f41d-410e-990b-32bc20e4b662 · outbound

This paper cites Robust markov decision processes.

Linear Mixture Distributionally Robust Markov Decision Processes Robust markov decision processes

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:21.282437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:14.366644Z digest=sha256:a4b669369d53e2db70361498f392aa404ccda7ee5855982facfda10399da108e

Observation 5cd48174-e78c-4762-9c2e-2fc32074bb3d · outbound

This paper cites Mutual alignment transfer learning.

Linear Mixture Distributionally Robust Markov Decision Processes Mutual alignment transfer learning

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:21.148819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:14.512261Z digest=sha256:60050ae7a139cb73c0faa58e3b01dc048ebdf602395e2a2734f29837e9a43055

Observation f019cdc2-eb5f-4a4f-b270-8c78b598182f · outbound

This paper cites The robustness-performance tradeoff in markov decision processes.

Linear Mixture Distributionally Robust Markov Decision Processes The robustness-performance tradeoff in markov decision processes

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:20.851485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:14.695106Z digest=sha256:414fbc3414b1d1d75c3445c247e6962962971651548f401df2eca4e4dd767bff

Observation 4c789318-dc3a-46c2-942a-78328245af70 · outbound

This paper cites Improved sample complexity bounds for distributionally robust reinforcement learning.

Linear Mixture Distributionally Robust Markov Decision Processes Improved sample complexity bounds for distributionally robust reinforcement learning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:20.560793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:14.854406Z digest=sha256:cbc8a38b4ddf80ae539ab33547609f81373d7627586fe8d7e4be6be9b531ccb5

Observation 48fe2c9b-761c-4683-95c9-cbe1ca36377a · outbound

This paper cites Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics.The Annals of Statistics, 50 (6):3223–3248, 2022.

Linear Mixture Distributionally Robust Markov Decision Processes Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics.The Annals of Statistics, 50 (6):3223–3248, 2022

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:20.325566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.061034Z digest=sha256:9f788d80a61c11bb1a13c84c66a4c02f90930c1d022998e670984316699a60e5

Observation 9ea4557c-3d4f-4c06-8821-0998a738ab90 · outbound

This paper cites Reward-free model-based reinforcement learning with linear function approximation.Advances in Neural Information Processing Systems, 34:1582–1593, 2021.

Linear Mixture Distributionally Robust Markov Decision Processes Reward-free model-based reinforcement learning with linear function approximation.Advances in Neural Information Processing Systems, 34:1582–1593, 2021

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:20.071385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.218794Z digest=sha256:375a551c55587c0864227e72e7496213c82d49d7a0c59b0fad6afc7efd023113

Observation c9bd51f5-0c82-4c63-b83e-3b55b28179df · outbound

This paper cites Learning adversarial linear mixture markov decision processes with bandit feedback and unknown transition.

Linear Mixture Distributionally Robust Markov Decision Processes Learning adversarial linear mixture markov decision processes with bandit feedback and unknown transition

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:19.715374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.452113Z digest=sha256:ac17f174dad2d3800f6e9dd63f81e361da6341d44ff292dd655c5fd612198b02

Observation 64543293-787a-4e7d-9421-ba5098cc83b2 · outbound

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

Linear Mixture Distributionally Robust Markov Decision Processes Sim-to-real transfer in deep reinforcement learning for robotics: a survey

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:19.344128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.598520Z digest=sha256:7765acc168011d007cf72e638296d5a001068c246ecd8016f783b3d00eb53483

Observation c31db8ea-24ae-41f8-9c98-2f9ac1ce89a9 · outbound

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

Linear Mixture Distributionally Robust Markov Decision Processes Nearly minimax optimal reinforcement learning for linear mixture markov decision processes

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:18.984048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.708272Z digest=sha256:1c683c92d9a31c8fca977a0cf1e25238e16e8a4fa20bf9435f7b7fa87b2d03c1

Observation e07a3f5e-17ce-4ad3-bd72-8388f5f6787a · outbound

This paper cites Provably efficient reinforcement learning for discounted mdps with feature mapping.

Linear Mixture Distributionally Robust Markov Decision Processes Provably efficient reinforcement learning for discounted mdps with feature mapping

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:18.616950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:15.877799Z digest=sha256:edd61d01ffc08230e4b15991b032dcd1fb297becd60722c119bcb6732b5b95d9

Observation 749cdfc5-2079-4a84-9f5c-0edfc4defa36 · outbound

This paper cites Natural actor-critic for robust reinforcement learning with function approximation.

Linear Mixture Distributionally Robust Markov Decision Processes Natural actor-critic for robust reinforcement learning with function approximation

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:18.244415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:16.093379Z digest=sha256:cea9c47dfc7e64fd5645459598798f7cfb658ede3832d94d418426a69bf06227

Observation e0055a81-ecff-4ada-b7a5-12dacbbec727 · outbound

This paper cites Finite-sample regret bound for distributionally robust offline tabular reinforcement learning.

Linear Mixture Distributionally Robust Markov Decision Processes Finite-sample regret bound for distributionally robust offline tabular reinforcement learning

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:17.858529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:16.285333Z digest=sha256:a3724286b7124cfa5116d8ac8809b6d0defec3aa4a6448466e19e0a1373c194e

Observation 832614ab-83b0-4b22-b97b-5e9906b73a12 · outbound

This paper cites Time- constrained robust mdps.Advances in Neural Information Processing Systems, 37:35574–35611,.

Linear Mixture Distributionally Robust Markov Decision Processes Time- constrained robust mdps.Advances in Neural Information Processing Systems, 37:35574–35611,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:17.527348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:16.401146Z digest=sha256:73d76ca47092c468c96fc2418526f3e3e82b51d5c7cfb2113f8aca410a7ce19d

Observation 5a065462-4302-4047-8c1a-f3386aceef79 · outbound

This paper cites A.1 Proof of Theorem 3.4 Proof.

Linear Mixture Distributionally Robust Markov Decision Processes A.1 Proof of Theorem 3.4 Proof

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:44:17.145142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:44:16.530086Z digest=sha256:49fe49305077ca2ae3c58f82020525e23e63ff3678255a2679e59d9cf34a0d7c

Pith citing papers

No inbound Pith citation observations are available.