Paper Citation Record · LEDGER

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces

As of 11 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2603.08287.

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

pith.paper-citation-record.v1
2603.08287 v3

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:42:51.246886Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

6 of 6 outbound references displayed

verified exact0
verified fuzzy0
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cae30dd-cab3-4bf6-bc34-c6737e1c1f8f · outbound

This paper cites Thus we have R1 = max(2σ p ds, p 16σ2 log(T))≤168α −1/2p max(C, σ2)(ds +d a) log(10(T+R a) max(1, L/C)).

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces Thus we have R1 = max(2σ p ds, p 16σ2 log(T))≤168α −1/2p max(C, σ2)(ds +d a) log(10(T+R a) max(1, L/C))

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.236520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f75e07d-dc61-4c25-9d21-0ea2000d86c8 · outbound

This paper cites Tail Bounds for Suprema of Gaussian Processes We prove the results given in Section 4.2.

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces Tail Bounds for Suprema of Gaussian Processes We prove the results given in Section 4.2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.226121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ede3f01-341e-4ada-8429-47e543160aaf · outbound

This paper cites lim n→∞ sup x∈Z (n) ∥f(x)∥ 2 # = lim n→∞ E.

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces lim n→∞ sup x∈Z (n) ∥f(x)∥ 2 # = lim n→∞ E

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.230986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9a8fb10-80b5-4d1a-b1b0-a30f9ca58f05 · outbound

This paper cites I{A} NX n=1 V Mn πn,1 (sn,1)−V M⋆ πn,1(sn,1) # ≤E.

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces I{A} NX n=1 V Mn πn,1 (sn,1)−V M⋆ πn,1(sn,1) # ≤E

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.241426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0186a3d0-5402-4e9c-8fe4-6af4e92b26d1 · outbound

This paper cites Using Lemma E.2, one can prove a version of the elliptical potential lemma that accounts for the fact that f (n) is only re-sampled at the end of each episode.

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces Using Lemma E.2, one can prove a version of the elliptical potential lemma that accounts for the fact that f (n) is only re-sampled at the end of each episode

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.246886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35365608-6bcb-4616-a0d1-236bf7c147ca · outbound

This paper cites A Short Note on the Relationship of Information Gain and Eluder Dimension.

Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces A Short Note on the Relationship of Information Gain and Eluder Dimension

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-02T18:42:51.219935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.