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Paper Citation Record · LEDGER

Distances for Markov chains from sample streams

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2505.18005.

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

pith.paper-citation-record.v1
2505.18005 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:43:11.520892Z

measured 49 of 49 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T08:05:55.418491Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:06:47.657564Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact1
  • verified fuzzy36
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e72f758d-64b1-4110-ba69-b3f8506d90c2 · outbound

This paper cites Near-linear time approximation algorithms for optimal transport via S inkhorn iteration.

Distances for Markov chains from sample streams Near-linear time approximation algorithms for optimal transport via S inkhorn iteration

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:18.066721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.188558Z digest=sha256:44e4a856041ef4b5537aa761096d5da150ec567345553418cdba5aa70ab38287

Observation feabed67-1f25-40a2-b946-1cadf2a11e90 · outbound

This paper cites Wasserstein generative adversarial networks.

Distances for Markov chains from sample streams Wasserstein generative adversarial networks

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:17.863773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.248264Z digest=sha256:a894186e777dbc44683713b31134b0ab0f0606c6f19f3f06818e5be206d3b777

Observation 8b0c0abb-d21f-49a7-9ab4-65e0c383cbe1 · outbound

This paper cites Causal transport in discrete time and applications.

Distances for Markov chains from sample streams Causal transport in discrete time and applications

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:17.619256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.333401Z digest=sha256:147f7b00e195200b8249c23410dcf2b97af662c72ba5dc366bbfdcf9de6c85b1

Observation 281aee69-4e33-4248-987c-cd6f1ddd53c7 · outbound

This paper cites Stochastic optimization for regularized wasserstein estimators.

Distances for Markov chains from sample streams Stochastic optimization for regularized wasserstein estimators

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:17.437615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.397164Z digest=sha256:e059eec314f86c575ea8914161ce895d32ba29dd0d4993e655b742dff6bb6b1c

Observation dd68743e-25ae-46da-a264-aa7d3c845b7a · outbound

This paper cites Distances for M arkov chains, and their differentiation.

Distances for Markov chains from sample streams Distances for M arkov chains, and their differentiation

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:17.196710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.472841Z digest=sha256:6e6bb0ba77bfc78c9b6c9a97e158dffd6f6f12d46e2517a25c0a453c0d6b6e30

Observation 3b7617cd-d613-4547-a766-5407be4a5d3b · outbound

This paper cites Bisimulation metrics are optimal transport distances, and can be computed efficiently.

Distances for Markov chains from sample streams Bisimulation metrics are optimal transport distances, and can be computed efficiently

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:16.997601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.553519Z digest=sha256:24a8a6138a6777dc0827100c4c6f3b3776f9212958533c52ee9fea738f610817

Observation ff4e611f-aa39-4b87-985e-fbe45774505e · outbound

This paper cites Scalable methods for computing state similarity in deterministic M arkov decision processes.

Distances for Markov chains from sample streams Scalable methods for computing state similarity in deterministic M arkov decision processes

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:16.762703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.626075Z digest=sha256:6efb5f93b28af2ec5fbf6360826693f0eaaa304dffa1aba7f47c3f20d02433f7

Observation e84900c1-40c5-4715-bc9a-685195344550 · outbound

This paper cites Prediction, Learning, and Games.

Distances for Markov chains from sample streams Prediction, Learning, and Games

Reference 8

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unresolved
no resolver link, observed 2026-08-07T14:43:08.699935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:08.699935Z digest=sha256:f2e701e370bdcb2240b8857c55ac5ff565d42ecf355c68cf650f735e3641b2f5

Observation 53db93b7-d64e-40e8-8148-f758f5b3a27f · outbound

This paper cites On the complexity of computing probabilistic bisimilarity.

Distances for Markov chains from sample streams On the complexity of computing probabilistic bisimilarity

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:16.538394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.770975Z digest=sha256:4d9bbfe18ed5bcb9c17d889865e69350d037700055f39bc5e3816eab347918c8

Observation a257cb3e-9ac4-4380-9c94-08032629252a · outbound

This paper cites Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning.

Distances for Markov chains from sample streams Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning

Reference 10

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raw_fallback, observed 2026-08-07T14:43:16.322517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.823244Z digest=sha256:6c733933a9e7d28a6eb4f8d1901a7782e69248dd30d42551abc8cb863791f0ba

Observation fe68e7f0-0dfb-4270-9d41-313ba2e73ffb · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Distances for Markov chains from sample streams Sinkhorn distances: Lightspeed computation of optimal transport

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:16.150695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.906861Z digest=sha256:eff15ceab854ab4c58148b61acd556d1934fa945ac7005a69c8032c3ebe14e00

Observation ad1c96fa-5c98-4f8b-8931-a7617b75dd8a · outbound

This paper cites Metrics for labeled Markov systems.

Distances for Markov chains from sample streams Metrics for labeled Markov systems

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:16.016027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:08.988861Z digest=sha256:fbdd73ddfd2dc40bb78cd3e6ba77c51120c0d9be7fb7020f1acedd400406db94

Observation 37a391f6-3a6a-4286-9bf6-033fa0ea0535 · outbound

This paper cites The metric analogue of weak bisimulation for probabilistic processes.

Distances for Markov chains from sample streams The metric analogue of weak bisimulation for probabilistic processes

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T14:43:15.855104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.025199Z digest=sha256:eeba7670473a09d9d49acaebcf6137baca84ecf8c4a1acb9f0f3b8b503702cc0

Observation 2eb6f4ac-9844-43c7-9b7c-9a4ff8b8bc19 · outbound

This paper cites Provably Efficient RL with Rich Observations via Latent State Decoding.

Distances for Markov chains from sample streams Provably Efficient RL with Rich Observations via Latent State Decoding

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:15.676806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.114004Z digest=sha256:8ace9f33ebf6b739329cc30752d1fedc4d580fdcff4c8a516fa97ba686c29a4d

Observation 43480cf4-7c7c-4929-8b9e-649b4553a066 · outbound

This paper cites Computational methods for adapted optimal transport.

Distances for Markov chains from sample streams Computational methods for adapted optimal transport

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:15.471191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.182751Z digest=sha256:912e586ca0f00667cb3acb27e9c82bb82bb4bd9526fbf7d6e4c3fbd740b34955

Observation 55e0b47a-971c-4db6-b75a-90e13dc70b0b · outbound

This paper cites Learning with minibatch W asserstein: asymptotic and gradient properties.

Distances for Markov chains from sample streams Learning with minibatch W asserstein: asymptotic and gradient properties

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:15.260801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.255656Z digest=sha256:e86a39f8b398cf28c6639136824a89cadf7200160b0c300e34703588ec769766

Observation 8d9481bd-83e4-4c52-a489-b67a4d27371f · outbound

This paper cites Minibatch optimal transport distances; analysis and applications.

Distances for Markov chains from sample streams Minibatch optimal transport distances; analysis and applications

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:09.317303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:09.317303Z digest=sha256:e8fbd9fb16169615f2f5260d829aa50877868e0efc913bce05f2437e41b72be0

Observation 406a8d50-1458-4150-af8b-7a65d039b073 · outbound

This paper cites Metrics for finite Markov decision processes.

Distances for Markov chains from sample streams Metrics for finite Markov decision processes

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:15.047131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.408682Z digest=sha256:3f728d11740dcec123c56112219f5e7f1188eaca32b6b910910e6e1b978b1734

Observation f0ebf776-b0dd-47cf-8c18-c4af3cdc3e2f · outbound

This paper cites Stochastic Optimization for Large-scale Optimal Transport.

Distances for Markov chains from sample streams Stochastic Optimization for Large-scale Optimal Transport

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.902388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.495055Z digest=sha256:d60ab052fa485a8449ea27d6355468612ed015dc0f67e5c9242cda947be3b77c

Observation 42b16013-a2d2-4ef4-9cf0-e6baf5e24fee · outbound

This paper cites Learning generative models with S inkhorn divergences.

Distances for Markov chains from sample streams Learning generative models with S inkhorn divergences

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.756101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.576320Z digest=sha256:8ce934a58f45f07336dca7daab54116b48938e32024c728af22667f0d9024366

Observation 67f30d23-b620-4b44-a114-8d427d372df5 · outbound

This paper cites Equivalence notions and model minimization in Markov decision processes.

Distances for Markov chains from sample streams Equivalence notions and model minimization in Markov decision processes

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.602994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.649986Z digest=sha256:afaa43e22020270fcf279c423b8548ca6b8d6d8eb52ece600643a7b8e8af5378

Observation 6c9729fb-0d19-44ba-b573-c0b3f9f05102 · outbound

This paper cites A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning.

Distances for Markov chains from sample streams A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:43:11.669702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.711826Z digest=sha256:55b71c8bfd1a3058759cf15b6a4e08bf6b93a8f3dbc91f706303c05dd1e4178b

Observation 65099c39-ecb1-4bc5-96cf-8ab909b1ed19 · outbound

This paper cites Approximate policy iteration with bisimulation metrics.

Distances for Markov chains from sample streams Approximate policy iteration with bisimulation metrics

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.414863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.805005Z digest=sha256:0d24c51de32e32811f14c16daef9a63e8f14b490c3f75eb95e49847227de9a03

Observation fa8d0665-c73c-417f-a6b8-4b23069c9502 · outbound

This paper cites Empirical regularized optimal transport: Statistical theory and applications.

Distances for Markov chains from sample streams Empirical regularized optimal transport: Statistical theory and applications

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.233885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.873090Z digest=sha256:aaeeb8581c09627bd5df2348563735940a05df4e0cf77c35ae4b4fd361141d82

Observation b01a1bca-dbbf-4df1-9a9c-0186479c8b3c · outbound

This paper cites Causal Transport Plans and Their Monge–Kantorovich Problems.

Distances for Markov chains from sample streams Causal Transport Plans and Their Monge–Kantorovich Problems

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:14.010378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:09.944654Z digest=sha256:e681f1c63e2fc2f3c98e0ec3f1f61276edd680535f99d9b51928b1d842eda754

Observation 11af05b8-20d5-4a27-8dc0-fcd9f445b134 · outbound

This paper cites Continuous control with deep reinforcement learning.

Distances for Markov chains from sample streams Continuous control with deep reinforcement learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:10.014025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:10.014025Z digest=sha256:9b5d07bb3413f4f380d2ccfda5aa5d9effcf4a43ce2beba233d49e76a13c5369

Observation edd86c4b-e55c-4d88-bb49-881cf5a98e3b · outbound

This paper cites Online sinkhorn: Optimal transport distances from sample streams.

Distances for Markov chains from sample streams Online sinkhorn: Optimal transport distances from sample streams

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.853299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.089695Z digest=sha256:39249cd0e08937d861aa204f06ea786a098cc08477f761d89988c52540f4df85

Observation 3ce0b8e8-89e0-41d9-806a-dd4ee897a878 · outbound

This paper cites Communication and Concurrency.

Distances for Markov chains from sample streams Communication and Concurrency

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.679505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.168344Z digest=sha256:ed5fb992025cc1fd41bf8a25e4f927604149c811c49874beb7c9cfb88f32904a

Observation 459cb7ca-290c-4292-9638-e16b4602fb00 · outbound

This paper cites Bicausal optimal transport for M arkov chains via dynamic programming.

Distances for Markov chains from sample streams Bicausal optimal transport for M arkov chains via dynamic programming

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.538995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.227786Z digest=sha256:db7fc78302b61da5a2d82965b98b372996bbd02ba164483620f917610f56d877

Observation 466f974c-7315-414e-8400-ecdf58f350a8 · outbound

This paper cites Nemirovski, A.

Distances for Markov chains from sample streams Nemirovski, A

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.410443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.295079Z digest=sha256:81fcee7ccb87d2520693b3ac04c47d8b9a3a86a44f1b9a79cc2b95fc8ae3272c

Observation 60805593-1513-4802-a3c8-a281a7f76a95 · outbound

This paper cites Dealing with unbounded gradients in stochastic saddle-point optimization.

Distances for Markov chains from sample streams Dealing with unbounded gradients in stochastic saddle-point optimization

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.248063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.358913Z digest=sha256:70b9c339d199ff2a9c6a7b2f045e387f8f56f37aada93c665187df440a0ee348

Observation f93e72af-2394-43e1-bb2a-3b428faf8143 · outbound

This paper cites Optimal transport for stationary Markov chains via policy iteration.

Distances for Markov chains from sample streams Optimal transport for stationary Markov chains via policy iteration

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:13.040000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.427442Z digest=sha256:490997d800546f23c9cefe59942658c481dff9cf2b06be2eb45bd983b5908e17

Observation 4130639c-e7f9-4634-91d8-d66f8ce48594 · outbound

This paper cites Online Learning: A Modern Introduction Using Convex Optimization.

Distances for Markov chains from sample streams Online Learning: A Modern Introduction Using Convex Optimization

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:10.495407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:10.495407Z digest=sha256:2ffa82d1a78b0bda32270793331e59ef02e4bc416a3fdedcdbfd4ec4fb3f0888

Observation 259949c7-cec5-4336-af95-11d1c8352aa4 · outbound

This paper cites an unresolved cited work.

Distances for Markov chains from sample streams Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-08-07T14:43:12.864948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.533668Z digest=sha256:3bab06429c18a443fe19156cfcf3ec9569c71fa2d874ea46dd8ec041be476c1d

Observation f1855690-bcef-4773-9eb0-61491d2b174a · outbound

This paper cites Computational optimal transport.

Distances for Markov chains from sample streams Computational optimal transport

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:10.617890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:10.617890Z digest=sha256:dd99acecc44704b4014df97f9264d919ec76a9020eefc97507b6c219fb579adf

Observation 6f5a33c5-dd25-499e-b542-488abeca9a2c · outbound

This paper cites Pflug and Alois Pichler.

Distances for Markov chains from sample streams Pflug and Alois Pichler

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.718458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.699503Z digest=sha256:252fec9174c2c7f727c5f42bcc15f6c43866422d1e30c1007f74906f36c1b5a1

Observation a55cc13b-a6db-44ef-8846-3bd33af7e70e · outbound

This paper cites Puterman.

Distances for Markov chains from sample streams Puterman

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.543507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.772668Z digest=sha256:ec3514489e8f113b371c846fb27adbd60a49ec269034647f84a5e7c0290e388f

Observation 5092b065-ad0b-4366-8f97-e609299a26ef · outbound

This paper cites On equivalence of martingale tail bounds and deterministic regret inequalities.

Distances for Markov chains from sample streams On equivalence of martingale tail bounds and deterministic regret inequalities

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:10.849403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:10.849403Z digest=sha256:3df1890da9f41e362c6721dd6aa28dbe159c3cf548213505c796d7fce22e1890

Observation adcefea9-0976-44bb-ae9f-82e86569ce28 · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model.

Distances for Markov chains from sample streams Mastering atari, go, chess and shogi by planning with a learned model

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:10.919172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:10.919172Z digest=sha256:0fbf9425f3e9316b145ea117218b688eccb1361eb1f396967a9267bc9c7be27c

Observation a0aee30d-3a2e-4d0c-84c8-20684f45ce50 · outbound

This paper cites Large-scale optimal transport and mapping estimation.

Distances for Markov chains from sample streams Large-scale optimal transport and mapping estimation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.429078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:10.966445Z digest=sha256:41901bc763612dd86faf777db85494d29269e8aa15d179e03046ed49c7ebe95f

Observation 876bc801-2718-42e7-bd2a-a5afa8047804 · outbound

This paper cites High rank path development: an approach to learning the filtration of stochastic processes.

Distances for Markov chains from sample streams High rank path development: an approach to learning the filtration of stochastic processes

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.303887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:11.020452Z digest=sha256:725901a84ca4b82cf51aca8b07950df693f3e827d0f966cdcf4c8c672e4c0b96

Observation 420b38b7-7cc5-43ab-8087-d3888ca45278 · outbound

This paper cites Optimal Transport for structured data with application on graphs.

Distances for Markov chains from sample streams Optimal Transport for structured data with application on graphs

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.220627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:11.093694Z digest=sha256:98bdbce8e39fdb8e084d143a8dddcaa5d502f166b17af7ec7b194ec02bfd525f

Observation 1436d66d-37e5-4121-88b0-29a91229db9a · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Distances for Markov chains from sample streams Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:11.202344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:11.202344Z digest=sha256:c10db591e47f094ed9c2f4cbed11f30f1170b08a0f833259f2dbd0dfd5009529

Observation b9883963-f169-4682-a1df-aa5142583a4a · outbound

This paper cites An algorithm for quantitative verification of probabilistic transition systems.

Distances for Markov chains from sample streams An algorithm for quantitative verification of probabilistic transition systems

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:12.116659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:11.263758Z digest=sha256:381438474595a7988e5cde9381db3edaed693256c171e94b77f3830073dbffdf

Observation dc67b53b-4434-4222-b52f-792fba428993 · outbound

This paper cites Optimal transport: old and new, volume 338.

Distances for Markov chains from sample streams Optimal transport: old and new, volume 338

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T14:43:11.352997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:43:11.352997Z digest=sha256:dfee2fd42a5b388cb41a6644ca472e322d4d7fb8d463b94694e2f3d0be0c5912

Observation bab85e01-38ce-4b00-8197-7fa4c4d2edeb · outbound

This paper cites COT-GAN : Generating sequential data via causal optimal transport.

Distances for Markov chains from sample streams COT-GAN : Generating sequential data via causal optimal transport

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:11.976840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:11.441571Z digest=sha256:97108e8a357dca442c3d0169d3f51a26e8ba2e21d2e4f9e7b76d3967b1a8d1c5

Observation 687545e6-3214-4194-9321-152ed624df63 · outbound

This paper cites Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Distances for Markov chains from sample streams Learning Invariant Representations for Reinforcement Learning without Reconstruction

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:43:11.806941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T14:43:11.520892Z digest=sha256:48f9c115cc83edeebe4016051669d9313b50246ba89d03ab37795f91b7158193

Pith citing papers

Observation 0b983095-ce24-463e-a4d1-6194ecf64cfc · inbound

Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis cites this paper.

Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis Distances for Markov chains from sample streams

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-06-30T08:44:27.713865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T08:38:03.985543Z digest=sha256:a1cd216c59ee8da5a2c4883ec70856bb9230be07bd6e9aa54669991829720e5e

Observation d6e56aa1-edba-44d9-87ce-f839372e988d · inbound

Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization cites this paper.

Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization Distances for Markov chains from sample streams

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:06:47.658855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-02T08:05:55.418491Z digest=sha256:55c6c42819141c0eae58b3b9328b91b814859911104d2d919676ffdb701e9553