Paper Citation Record · LEDGER

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification

As of 10 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.00436.

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

pith.paper-citation-record.v1
2506.00436 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:08:42.891947Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

17 of 17 outbound references displayed

verified exact1
verified fuzzy15
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cf8c5fa9-cd11-45e3-8f0f-08ecb0f2051a · outbound

This paper cites Learning from positive and unlabeled data under the selected at random assumption.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Learning from positive and unlabeled data under the selected at random assumption

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:47.346398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50647642-ddf1-4e42-ba8f-8fa0c01f0211 · outbound

This paper cites Semi- supervised novelty detection.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Semi- supervised novelty detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:47.157322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b43c0ec-10f9-41ca-8ced-97a999daf9fb · outbound

This paper cites Niu, and Masashi Sugiyama.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Niu, and Masashi Sugiyama

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:46.940333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 39fd7824-0549-4777-a96e-259434ef0e7e · outbound

This paper cites Learning classifiers from only positive and unlabeled data.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Learning classifiers from only positive and unlabeled data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:46.684683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c6fbaa3-d6ea-43e4-bce6-abdbeac9c1a9 · outbound

This paper cites Non-negative bregman divergence minimization for deep direct density ratio estimation.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Non-negative bregman divergence minimization for deep direct density ratio estimation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:46.472084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 085736b7-0ed4-44c9-ac70-64a86fb74866 · outbound

This paper cites Learning from positive and unlabeled data with a selec- tion bias.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Learning from positive and unlabeled data with a selec- tion bias

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:46.311781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09b55321-46f6-45f6-925a-eaae0eafac65 · outbound

This paper cites Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:08:43.157102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0c88a026-8a93-47e3-bca6-c267410db4a3 · outbound

This paper cites Positive-unlabeled learning with non-negative risk estimator.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Positive-unlabeled learning with non-negative risk estimator

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:46.026681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a7f767b1-22b9-40a2-87f0-7df99622444a · outbound

This paper cites Case-control studies with contaminated controls.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Case-control studies with contaminated controls

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:45.633867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 944844c5-4a1b-4045-b3eb-d0a2b5e3ed94 · outbound

This paper cites Positive unlabeled learning for data stream classification.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Positive unlabeled learning for data stream classification

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:45.251761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1991277-3ce5-48cb-b878-d5f3b525f9a4 · outbound

This paper cites Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:45.006018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f8aba35b-19d5-493b-a25a-f34adf91bbc4 · outbound

This paper cites Positive unlabeled leaning for time series classification.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Positive unlabeled leaning for time series classification

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:44.848020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 185e1a5f-e3b6-4ee4-922c-1eb15ba510b8 · outbound

This paper cites Theoretical com- parisons of positive-unlabeled learning against positive- negative learning.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Theoretical com- parisons of positive-unlabeled learning against positive- negative learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:44.483420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8fcda59a-025e-43a4-a27f-10d429edfa15 · outbound

This paper cites an unresolved cited work.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:08:44.181674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0e76ebf8-eb83-4d0f-a19c-80b07e9567bf · outbound

This paper cites Novelty detection: Unlabeled data definitely help.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Novelty detection: Unlabeled data definitely help

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:43.947551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 556cc406-03d7-4d3f-8da7-5a5f96b06bf0 · outbound

This paper cites van der Vaart.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification van der Vaart

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:43.648462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3270f388-b5f2-4bed-aa7e-ecc8896b8a51 · outbound

This paper cites Presence-only data and the em algorithm.

Learning from Double Positive and Unlabeled Data for Potential-Customer Identification Presence-only data and the em algorithm

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:08:43.344036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.