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Paper Citation Record · LEDGER

Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1909.07192.

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

pith.paper-citation-record.v1
1909.07192 v1

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:06:01.980894Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T20:45:21.448495Z

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 31601813-8d3e-4133-9bb5-1b26b4854632 · inbound

Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy cites this paper.

Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:45:21.456810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-10T20:45:21.350695Z digest=sha256:7cb84904cfa4a33f61c152c6f72d82876626f6b2d362b2bd4f04d39f5fd737ea

Observation 994b0a1a-d179-438f-9c6b-68b0d6f5fff9 · inbound

Bounding Neyman-Pearson Region with $f$-Divergences cites this paper.

Bounding Neyman-Pearson Region with $f$-Divergences Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-15T22:06:01.980894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T22:06:01.980894Z digest=sha256:84c23fb43632960e8d625e2149a497f4616fdf3925771147517c22e2990acfbc