Paper Citation Record · LEDGER

Bounding Neyman-Pearson Region with $f$-Divergences

As of 23 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2505.08899.

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

pith.paper-citation-record.v1
2505.08899 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:06:02.008752Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

26 of 26 outbound references displayed

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verified fuzzy22
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76e6fc00-fb34-4714-9fed-f08bcf15e46a · outbound

This paper cites Methods of information geometry.

Bounding Neyman-Pearson Region with $f$-Divergences Methods of information geometry

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.408864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4f7fe5e8-7d5d-40ee-a49d-b6bd31da1051 · outbound

This paper cites Robust solutions of optimization problems affected by uncertain probabilities // Management Science.

Bounding Neyman-Pearson Region with $f$-Divergences Robust solutions of optimization problems affected by uncertain probabilities // Management Science

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.395448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4a77a95a-8695-4a33-bbda-33f31240b920 · outbound

This paper cites Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure // IEEE Transactions on Signal Processing.

Bounding Neyman-Pearson Region with $f$-Divergences Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure // IEEE Transactions on Signal Processing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.381870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 45e45bdc-06db-4e69-ab71-77b40f5e77ad · outbound

This paper cites On Stein’s lemma in hypotheses testing in general non-asymptotic case // Statistical Inference for Stochastic Processes.

Bounding Neyman-Pearson Region with $f$-Divergences On Stein’s lemma in hypotheses testing in general non-asymptotic case // Statistical Inference for Stochastic Processes

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.367951Z

Source-reported events for the cited work

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Observation 94260410-6c61-4d5e-b530-47acfbc5eca2 · outbound

This paper cites Wasserstein upper bounds of the total variation for smooth densities // Statistics & Probability Letters.

Bounding Neyman-Pearson Region with $f$-Divergences Wasserstein upper bounds of the total variation for smooth densities // Statistics & Probability Letters

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.354087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9cda20c7-e844-4fb4-94cd-6c92d13aebba · outbound

This paper cites A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations // Annals of Mathematical Statistics.

Bounding Neyman-Pearson Region with $f$-Divergences A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations // Annals of Mathematical Statistics

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.339691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0aab7a82-bda7-4c5f-a9e0-b78051d4a2fe · outbound

This paper cites Differential and Integral Calculus, Volume 2.

Bounding Neyman-Pearson Region with $f$-Divergences Differential and Integral Calculus, Volume 2

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.325851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b39244f9-4843-4ff5-9a0f-ceed803e9905 · outbound

This paper cites a t von Markoffschen Ketten // A Magyar Tudom \'a nyos Akad \'e mia Matematikai Kutat \'o Int \'e zet \'e nek K \.

Bounding Neyman-Pearson Region with $f$-Divergences a t von Markoffschen Ketten // A Magyar Tudom \'a nyos Akad \'e mia Matematikai Kutat \'o Int \'e zet \'e nek K \

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.311905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ece87b22-b40d-4d93-b7dc-343d4bfc4f2a · outbound

This paper cites Empirical Squared Hellinger Distance Estimator and Generalizations to a Family of -Divergence Estimators // Entropy.

Bounding Neyman-Pearson Region with $f$-Divergences Empirical Squared Hellinger Distance Estimator and Generalizations to a Family of -Divergence Estimators // Entropy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.297556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec6e4136-2e53-44c3-b185-7fab25fd4fe7 · outbound

This paper cites Refinements of Pinsker's inequality // IEEE Transactions on Information Theory.

Bounding Neyman-Pearson Region with $f$-Divergences Refinements of Pinsker's inequality // IEEE Transactions on Information Theory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.283497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 30e1cd76-d589-41b2-8cef-c29c95c729ff · outbound

This paper cites On choosing and bounding probability metrics // International statistical review.

Bounding Neyman-Pearson Region with $f$-Divergences On choosing and bounding probability metrics // International statistical review

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.269757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3073a27f-272c-492e-ab86-4307533a69a1 · outbound

This paper cites Quantification method of classification processes.

Bounding Neyman-Pearson Region with $f$-Divergences Quantification method of classification processes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.255927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 48af5ae9-5ee8-47b1-a911-45e42f374165 · outbound

This paper cites Probability of error, equivocation, and the Chernoff bound // IEEE Trans.

Bounding Neyman-Pearson Region with $f$-Divergences Probability of error, equivocation, and the Chernoff bound // IEEE Trans

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.241504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9b2ebc7f-4aaa-4710-a03c-5bad75df8080 · outbound

This paper cites an unresolved cited work.

Bounding Neyman-Pearson Region with $f$-Divergences Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:06:02.227291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a758228d-c4ef-44f8-9473-9137f62640c9 · outbound

This paper cites Demystifying the optimal performance of multi-class classification // Advances in Neural Information Processing Systems.

Bounding Neyman-Pearson Region with $f$-Divergences Demystifying the optimal performance of multi-class classification // Advances in Neural Information Processing Systems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.213329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 67424c50-e879-4384-b797-2a93855387b3 · outbound

This paper cites The divergence and Bhattacharyya distance measures in signal selection // IEEE transactions on communication technology.

Bounding Neyman-Pearson Region with $f$-Divergences The divergence and Bhattacharyya distance measures in signal selection // IEEE transactions on communication technology

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.199395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 23b373d6-9b43-4d92-93fd-d607faf8f4df · outbound

This paper cites Imitation learning as f-divergence minimization // Algorithmic Foundations of Robotics XIV: Proceedings of the Fourteenth Workshop on the Algorithmic Foundations of Robotics 14.

Bounding Neyman-Pearson Region with $f$-Divergences Imitation learning as f-divergence minimization // Algorithmic Foundations of Robotics XIV: Proceedings of the Fourteenth Workshop on the Algorithmic Foundations of Robotics 14

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.185339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0fc6af8d-b5e0-46a2-9d8c-41be03a96b87 · outbound

This paper cites an unresolved cited work.

Bounding Neyman-Pearson Region with $f$-Divergences Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:06:02.171121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5884e6a2-f2a0-41b4-9649-2fa75605c2f2 · outbound

This paper cites Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization // Advances in neural information processing systems.

Bounding Neyman-Pearson Region with $f$-Divergences Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization // Advances in neural information processing systems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.156088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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

This paper cites Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data.

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

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Observation c5e0a437-80a9-4201-9426-0a7c896c728a · outbound

This paper cites f-gan: Training generative neural samplers using variational divergence minimization // Advances in neural information processing systems.

Bounding Neyman-Pearson Region with $f$-Divergences f-gan: Training generative neural samplers using variational divergence minimization // Advances in neural information processing systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.139761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d95e82f1-6ca5-4470-8769-eff9a51077da · outbound

This paper cites The sample complexity of simple binary hypothesis testing // The Thirty Seventh Annual Conference on Learning Theory.

Bounding Neyman-Pearson Region with $f$-Divergences The sample complexity of simple binary hypothesis testing // The Thirty Seventh Annual Conference on Learning Theory

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.124121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3b71c44-2052-4523-8931-95c14f9c9a65 · outbound

This paper cites Estimation of information theoretic measures for continuous random variables // Advances in neural information processing systems.

Bounding Neyman-Pearson Region with $f$-Divergences Estimation of information theoretic measures for continuous random variables // Advances in neural information processing systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.108402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b60557c7-3158-4d18-8973-51fa7f535393 · outbound

This paper cites f -divergence Inequalities // IEEE Transactions on Information Theory.

Bounding Neyman-Pearson Region with $f$-Divergences f -divergence Inequalities // IEEE Transactions on Information Theory

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.092942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c4dc904-163f-47ee-a49e-14e4ddb88028 · outbound

This paper cites Evaluating state-of-the-art classification models against Bayes optimality // Proceedings of the 35th International Conference on Neural Information Processing Systems.

Bounding Neyman-Pearson Region with $f$-Divergences Evaluating state-of-the-art classification models against Bayes optimality // Proceedings of the 35th International Conference on Neural Information Processing Systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:02.076996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f549c0ef-2db6-4c81-8ddf-5fb06958979c · outbound

This paper cites Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints.

Bounding Neyman-Pearson Region with $f$-Divergences Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:06:02.008752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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