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Paper Citation Record · LEDGER

What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1903.01620.

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

pith.paper-citation-record.v1
1903.01620 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:35.698648Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:56:27.589945Z

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 b2b7ab68-b52d-46e3-9e31-beb733583206 · inbound

Learning High-dimensional Gaussians from Censored Data cites this paper.

Learning High-dimensional Gaussians from Censored Data What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-16T06:04:35.698648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T06:04:35.698648Z digest=sha256:c6e764dc49aaac7285f2a5c80a0e57d3eefc323a3fbcd844efa1491f2abf10de

Observation e290a934-aa8e-4a2f-9385-017d6be4289d · inbound

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data cites this paper.

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-07-04T23:40:47.695530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-12T01:31:00.029032Z digest=sha256:b946c22d9ca0ef1bdb5043f4ffd80e5e66bd7fc5e640431c1019db32745dde51