Paper Citation Record · LEDGER

Bias as a Virtue: Rethinking Generalization under Distribution Shifts

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.00407.

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

pith.paper-citation-record.v1
2506.00407 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:11:23.890712Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

39 of 39 outbound references displayed

verified exact0
verified fuzzy25
unresolved13
parse uncertain1
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74dd3b96-9572-4823-bb1b-4fd95376a789 · outbound

This paper cites Invariant Risk Minimization.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Invariant Risk Minimization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:20.456057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b50d90b-29ec-429f-8de3-b8d6ab858fc9 · outbound

This paper cites A survey of cross-validation procedures for model selection.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts A survey of cross-validation procedures for model selection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:28.711404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f1983ad-662d-4f71-b372-f24c11ca8004 · outbound

This paper cites Curriculum learning.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Curriculum learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:28.542313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78029fb3-8764-4da8-8fef-6b3307d48a59 · outbound

This paper cites Ellis, Brian Whitman, and Paul Lamere.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Ellis, Brian Whitman, and Paul Lamere

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:28.404378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d88d155b-7fae-45ad-9705-fef7310ba33b · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Sinkhorn distances: Lightspeed computation of optimal transport

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:20.782244Z

Source-reported events for the cited work

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Observation 44cc8bec-fded-441a-91a3-7bfeebf55772 · outbound

This paper cites Underspecification Presents Challenges for Credibility in Modern Machine Learning.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Underspecification Presents Challenges for Credibility in Modern Machine Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:20.859619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cddcf9f-ab56-447a-b001-02c261f3a8e1 · outbound

This paper cites A unified bias-variance decomposition.Proceedings of 17th International Conference on Machine Learning, pages 231–238, 2000.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts A unified bias-variance decomposition.Proceedings of 17th International Conference on Machine Learning, pages 231–238, 2000

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:28.260775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ab14b424-0674-4631-9493-694baef7497b · outbound

This paper cites Learning models with uniform performance via distributionally robust optimization.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Learning models with uniform performance via distributionally robust optimization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:28.132084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02519bce-d1d4-4448-a91d-f8b2819c1871 · outbound

This paper cites Domain-adversarial training of neural networks.The Journal of Machine Learning Research, 17(1):2096–2030, 2016.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Domain-adversarial training of neural networks.The Journal of Machine Learning Research, 17(1):2096–2030, 2016

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.975186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0639a4c-0052-4bab-bd7a-0c3b575560ee · outbound

This paper cites Neural message passing for quantum chemistry.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Neural message passing for quantum chemistry

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:21.209118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4616b34b-dbf3-4897-8926-e048da29091e · outbound

This paper cites Au- tomated curriculum learning for neural networks.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Au- tomated curriculum learning for neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.832174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55073d6e-7a52-41db-8e20-cb32a2ddc07d · outbound

This paper cites In search of lost domain generalization.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts In search of lost domain generalization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:21.358275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d0ccdc1-e32a-4529-afd4-d3649a4e2cab · outbound

This paper cites On the power of curriculum learning in training deep networks.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts On the power of curriculum learning in training deep networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.690517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4661b1d5-791f-4f36-865b-a2fff86f961e · outbound

This paper cites Feature-level ensembling for both accuracy and robustness.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Feature-level ensembling for both accuracy and robustness

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.528683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c64463e0-c4d5-4e44-93c3-11a15f275851 · outbound

This paper cites Variance and bias for general loss functions.Machine learning, 51(2):115–135, 2003.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Variance and bias for general loss functions.Machine learning, 51(2):115–135, 2003

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.334447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abcd9c00-5fc1-4407-b52b-3dd70f93cee3 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:27.152214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df3870a2-9792-439b-83f1-89fc4f0e30ec · outbound

This paper cites Adam: A method for stochastic optimization.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Adam: A method for stochastic optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:21.789068Z

Source-reported events for the cited work

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Observation 159e0be7-7d60-403f-a055-d8ac47b0d33b · outbound

This paper cites Auto-encoding variational bayes.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Auto-encoding variational bayes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:26.979398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 555ea7cc-cf4a-432d-a61e-7331245ffcb0 · outbound

This paper cites Wilds: A benchmark of in-the-wild distribution shifts.International Conference on Machine Learning, pages 5637–5664, 2021.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Wilds: A benchmark of in-the-wild distribution shifts.International Conference on Machine Learning, pages 5637–5664, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:26.762740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c5164bd-fe72-4cf3-b34c-08560234a48d · outbound

This paper cites Fine-tuning can distort pretrained features and underperform out-of-distribution.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Fine-tuning can distort pretrained features and underperform out-of-distribution

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:26.559043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 41fb516c-02de-4c41-bac8-b113b2da5624 · outbound

This paper cites Rdkit: Open-source cheminformatics.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Rdkit: Open-source cheminformatics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:26.389683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94116843-bba6-49b9-a839-192d269fd5bc · outbound

This paper cites Improved Complexity Analysis of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Improved Complexity Analysis of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:22.380069Z

Source-reported events for the cited work

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Observation 740ebaa4-a395-4645-8d38-7f22411d4e1f · outbound

This paper cites Prioritized training on points that are learnable, worth learning, and not yet learnt.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Prioritized training on points that are learnable, worth learning, and not yet learnt

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:26.034460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e61c23c-0d33-4eb5-942d-a011ae8f55a0 · outbound

This paper cites A unifying view on dataset shift in classification.Pattern recognition, 45(1):521–530, 2012.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts A unifying view on dataset shift in classification.Pattern recognition, 45(1):521–530, 2012

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:22.531136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37991926-52da-4400-813a-d52fa54dbef8 · outbound

This paper cites The generation of a unique machine description for chemical structures- a technique developed at chemical abstracts service.Journal of Chemical Documentation, 5(2):107–113, 1965.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts The generation of a unique machine description for chemical structures- a technique developed at chemical abstracts service.Journal of Chemical Documentation, 5(2):107–113, 1965

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:22.618880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f590ade9-6f8f-4c40-8485-e4f309eb580b · outbound

This paper cites an unresolved cited work.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:11:25.872770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d85443ec-dba3-4f9b-aa79-28f522ce26fb · outbound

This paper cites Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:25.655009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 531bc1df-45a1-4168-8692-225b209a2e80 · outbound

This paper cites An investigation of why overparameterization exacerbates spurious correlations.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts An investigation of why overparameterization exacerbates spurious correlations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:25.509570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f14426de-bf50-4d01-9947-f3e6173a761a · outbound

This paper cites Breeds: Benchmarks for sub- population shift.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Breeds: Benchmarks for sub- population shift

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:25.342679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d701315-5156-4baa-9e41-87906f713935 · outbound

This paper cites Toward causal representation learning.Proceedings of the IEEE, 109(5):612–634, 2021.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Toward causal representation learning.Proceedings of the IEEE, 109(5):612–634, 2021

Reference 30

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Source-reported events for the cited work

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Observation a277e8c6-be0e-47b5-987b-cf62375718ee · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Active learning for convolutional neural networks: A core-set approach

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:23.232869Z

Source-reported events for the cited work

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Observation f9add327-8080-4429-9feb-19e07e3fcfe5 · outbound

This paper cites Active learning literature survey.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Active learning literature survey

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:25.187289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b04b24ba-dd8d-45d6-9925-0b37389db5be · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Towards Out-Of-Distribution Generalization: A Survey

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T12:11:23.421416Z

Source-reported events for the cited work

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Observation bb1cf2fb-9405-4f41-acf0-fc264d36d38a · outbound

This paper cites Measuring robustness to natural distribution shifts in image classification.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Measuring robustness to natural distribution shifts in image classification

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:24.967744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3ee9d6b-cc7b-434a-8881-67bc18980fce · outbound

This paper cites Evading the simplicity bias: Training robust ensembles with random weights.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Evading the simplicity bias: Training robust ensembles with random weights

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:24.784241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 327bfa04-4c7d-4f7e-84c3-fc4125d5629c · outbound

This paper cites Adversarial discriminative domain adaptation.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Adversarial discriminative domain adaptation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:24.562240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 229a4196-9725-4b55-97e7-de4b4a88baf7 · outbound

This paper cites A fine-grained analysis on distribution shift.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts A fine-grained analysis on distribution shift

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:24.346503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a49f461a-f5b0-4081-aa84-fbb9c8295f0d · outbound

This paper cites Data-centric artificial intelligence: A survey.IEEE Transactions on Knowledge and Data Engineering, 35(12):11442–11458, 2023.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Data-centric artificial intelligence: A survey.IEEE Transactions on Knowledge and Data Engineering, 35(12):11442–11458, 2023

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:11:24.152062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 873a6495-dc76-4421-86c9-4967c1845b0a · outbound

This paper cites an unresolved cited work.

Bias as a Virtue: Rethinking Generalization under Distribution Shifts Unresolved cited work

Reference 2013

Resolution: parse uncertain; raw_fallback, observed 2026-08-07T12:11:26.204652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.