Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.14116.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:29:57.707840Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:21:09.907842Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 9df9494a-451c-4972-af75-8c31c1fe651d · inbound
Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control cites this paper.
Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:29:57.707840Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5873d381-d331-409c-a1e3-2eb8916b7258 · inbound
Approximations and Learning for Decentralized Stochastic Control and Near Optimal Finite Window Policies cites this paper.
Approximations and Learning for Decentralized Stochastic Control and Near Optimal Finite Window Policies Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-07-24T02:23:48.102139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.