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Paper Citation Record · LEDGER

Statistical Learning Theory for Control: A Finite Sample Perspective

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.05423.

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

pith.paper-citation-record.v1
2209.05423 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:51:50.835762Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:56:56.105069Z

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 79758392-6562-46a7-aaff-13211f263783 · inbound

Non-Asymptotic Bounds for Closed-Loop Identification of Unstable Nonlinear Stochastic Systems cites this paper.

Non-Asymptotic Bounds for Closed-Loop Identification of Unstable Nonlinear Stochastic Systems Statistical Learning Theory for Control: A Finite Sample Perspective

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T21:51:50.835762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:51:50.835762Z digest=sha256:8853b248674441ccb659fd0c0c01f7cc35aae02840cde9bcb8b47d7fd9833e4c

Observation 8d4315b4-0a11-4778-bed7-d99983c00933 · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Statistical Learning Theory for Control: A Finite Sample Perspective

Reference 262

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:56:56.106503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-28T02:35:39.845487Z digest=sha256:dfd49520d440f5c64549b531559e5711e7a1ac56a3d8140b1c31c296449f7268