Paper Citation Record · LEDGER

Nearly Optimal Sample Complexity for Learning with Label Proportions

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2505.05355.

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

pith.paper-citation-record.v1
2505.05355 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:16:11.191984Z

measured 13 of 13 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 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

13 of 13 outbound references displayed

verified exact0
verified fuzzy11
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 012d2bc9-4280-4a3c-a562-66b0bc0ae86e · outbound

This paper cites Higgs: The Higgs dataset consists of Monte-Carlo simulated particle accelerator data, where the goal is to distinguish between processes that create Higgs bosons and that do not.

Nearly Optimal Sample Complexity for Learning with Label Proportions Higgs: The Higgs dataset consists of Monte-Carlo simulated particle accelerator data, where the goal is to distinguish between processes that create Higgs bosons and that do not

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.341195Z

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.

Observation f9b6bb46-70e2-4e68-a586-ad6960be08bb · outbound

This paper cites K., and Sugiyama, M.

Nearly Optimal Sample Complexity for Learning with Label Proportions K., and Sugiyama, M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.448287Z

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.

Observation 0ccdafae-8f84-4635-be7a-accc1cc4eb3b · outbound

This paper cites probability of error.

Nearly Optimal Sample Complexity for Learning with Label Proportions probability of error

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.386664Z

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.

Observation 541f1b72-6d5a-41b6-9d62-b7470f00ae85 · outbound

This paper cites In order to achieve fast rates, we will investigate loss differences.

Nearly Optimal Sample Complexity for Learning with Label Proportions In order to achieve fast rates, we will investigate loss differences

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.371993Z

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.

Observation 9ff8def7-72fd-4205-b8b5-ea4180210b7c · outbound

This paper cites First, we note that E[ℓj(w)] =L(w); this follows directly from Equation (3).

Nearly Optimal Sample Complexity for Learning with Label Proportions First, we note that E[ℓj(w)] =L(w); this follows directly from Equation (3)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.356793Z

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.

Observation ad223459-9d8a-4c17-9c23-038372329709 · outbound

This paper cites Improving the sample complexity using global data.

Nearly Optimal Sample Complexity for Learning with Label Proportions Improving the sample complexity using global data

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.433291Z

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.

Observation fc29ceaa-5339-4601-9ac4-fad85c758092 · outbound

This paper cites Supervised learning by training on aggregate outputs.

Nearly Optimal Sample Complexity for Learning with Label Proportions Supervised learning by training on aggregate outputs

Reference 2002

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.418236Z

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.

Observation f0d6e49e-83d4-4793-933a-7237c7dc3b05 · outbound

This paper cites Deep learning from label proportions for emphysema quantification.

Nearly Optimal Sample Complexity for Learning with Label Proportions Deep learning from label proportions for emphysema quantification

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.495337Z

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.

Observation 3b6accd7-98c6-45f9-a50e-2932fe8544a1 · outbound

This paper cites and Kuck, H.

Nearly Optimal Sample Complexity for Learning with Label Proportions and Kuck, H

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.477201Z

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.

Observation dd3c0bea-fc01-4fdf-b46b-d60aee7077ec · outbound

This paper cites doi: 10.1109/tit.2010.

Nearly Optimal Sample Complexity for Learning with Label Proportions doi: 10.1109/tit.2010

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-15T23:16:11.133223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f13a7d-0362-4ff7-b6ef-9895ebbaf3d5 · outbound

This paper cites Deep multi-class learning from label proportions.

Nearly Optimal Sample Complexity for Learning with Label Proportions Deep multi-class learning from label proportions

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T23:16:11.147997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8218866-cfd0-484d-aadd-317ef99106ac · outbound

This paper cites Binomial and poisson distributions as maxi- mum entropy distributions.

Nearly Optimal Sample Complexity for Learning with Label Proportions Binomial and poisson distributions as maxi- mum entropy distributions

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.463321Z

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.

Observation 79dce129-5a14-45f4-be42-d8d6864ad160 · outbound

This paper cites and Zhang, J.

Nearly Optimal Sample Complexity for Learning with Label Proportions and Zhang, J

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:16:11.401850Z

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.

Pith citing papers

No inbound Pith citation observations are available.