Paper Citation Record · LEDGER

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2510.19328.

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

pith.paper-citation-record.v1
2510.19328 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:46:36.942538Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

40 of 40 outbound references displayed

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unresolved38
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External citation measurements

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Outbound references

Observation fce4aaa9-a724-43aa-9228-15da6125f23e · outbound

This paper cites Advances in Neural Information Processing Systems34, 15682–15694 (2021).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Advances in Neural Information Processing Systems34, 15682–15694 (2021)

Reference 1

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Observation 31f4b9f1-dffb-486f-a4b9-991461a86088 · outbound

This paper cites Journal of Neurosurgery: Spine32(6), 985– 987 (2020).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Journal of Neurosurgery: Spine32(6), 985– 987 (2020)

Reference 2

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Observation 220aa73c-3103-48f2-8e61-99ed3f4ea965 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 3

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Observation 1e94fe24-0e3f-4ef5-871f-383b9651efb3 · outbound

This paper cites Posterior calibration and exploratory analysis for natural language processing models.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Posterior calibration and exploratory analysis for natural language processing models

Reference 4

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Observation 76f0b78e-53ab-4145-a376-8e9c0e36b663 · outbound

This paper cites an unresolved cited work.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Unresolved cited work

Reference 5

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Observation 172ccb45-f6ce-499e-b35c-7ff9b68cc883 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: International Conference on Machine Learning, pp

Reference 6

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Observation ea9ee65c-0512-4d04-b3f2-96961b501990 · outbound

This paper cites Electronic Journal of Statistics11, 5052–5080 (2017).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Electronic Journal of Statistics11, 5052–5080 (2017)

Reference 7

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Observation d6b016b5-ee1a-4d0a-b6d9-c29d5c6a67b8 · outbound

This paper cites Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

Reference 8

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Observation e55e9c35-7cb3-4dec-bf83-d53361c974bf · outbound

This paper cites In: Icml, vol.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Icml, vol

Reference 9

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Observation 4e8adf6d-b545-481f-b69f-daf07d7596b8 · outbound

This paper cites In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp

Reference 10

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Observation 3d5c2f25-f7dd-4fb5-a2fc-9514810ea94a · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2022, 3673–3684 (2022).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Findings of the Association for Computational Linguistics: ACL 2022, 3673–3684 (2022)

Reference 11

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Observation 2f8c2c3e-49d2-4aa3-b32d-146454c3c938 · outbound

This paper cites In: 2022 30th European Signal Processing Conference (EUSIPCO), pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: 2022 30th European Signal Processing Conference (EUSIPCO), pp

Reference 12

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Observation 6618e819-c680-4ac7-a6af-bb27ad14b523 · outbound

This paper cites In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp

Reference 13

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Observation 552e18da-e537-4a7c-8f7b-353778215e6b · outbound

This paper cites Advances in Neural Information Processing Systems32(2019).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Advances in Neural Information Processing Systems32(2019)

Reference 14

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Observation ed7ed198-cd53-45b7-ba9f-81a44011387e · outbound

This paper cites In: International Conference on Machine Learning, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: International Conference on Machine Learning, pp

Reference 15

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Observation 8432a4d1-d5c6-407f-b12a-c8c627847451 · outbound

This paper cites In: 2021 29th European Signal Processing Conference (EUSIPCO), pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: 2021 29th European Signal Processing Conference (EUSIPCO), pp

Reference 16

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Observation 0430a46c-b3b6-4241-a646-b66dccd038af · outbound

This paper cites In: International Con- ference on Machine Learning, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: International Con- ference on Machine Learning, pp

Reference 17

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Observation a4635bfb-5e18-40a5-ad04-aed928e7cbb1 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: International Conference on Machine Learning, pp

Reference 18

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Observation 1f582f02-72a7-4c1d-9934-796a04b4ecbe · outbound

This paper cites In: International Conference on Machine Learning, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: International Conference on Machine Learning, pp

Reference 19

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Observation 08f4f099-c403-44de-9500-8ae4a3612e1c · outbound

This paper cites DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

Reference 20

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Observation f96932b6-4002-4f74-9536-fee660acbd4d · outbound

This paper cites In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R

Reference 21

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Observation 80dbd51b-9bbc-4913-9a92-35e376e53d98 · outbound

This paper cites In: Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pp

Reference 22

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Observation fceff157-16a0-41f1-b4c4-87c61d07e653 · outbound

This paper cites PhysioNet (2020).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations PhysioNet (2020)

Reference 23

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Observation 859368b6-4185-4336-bd49-423fde5af5a8 · outbound

This paper cites Kaggle (2020).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Kaggle (2020)

Reference 24

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Observation d104a51f-d83c-4a6d-8795-77ccd1ed4760 · outbound

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations UCI Machine Learning Repository

Reference 25

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations UCI Machine Learning Repository

Reference 26

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations UCI Machine Learning Repository

Reference 27

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations UCI Machine Learning Repository

Reference 28

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Observation e39dab54-6a9f-456c-ad6b-b4dd7dd08164 · outbound

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations IEEE Dataport (2023)

Reference 29

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Kaggle (2014)

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Unresolved cited work

Reference 31

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This paper cites In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp

Reference 32

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp

Reference 33

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations IEEE transactions on information theory28(2), 129–137 (1982)

Reference 34

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Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Journal of the American statistical association58(301), 236–244 (1963)

Reference 35

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Observation 50d9a455-b062-4ce8-9264-a29d55da4cdc · outbound

This paper cites Journal of Machine Learning Research9(86), 2579–2605 (2008).

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Journal of Machine Learning Research9(86), 2579–2605 (2008)

Reference 36

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This paper cites In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp

Reference 37

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This paper cites Machine Learning with a Reject Option: A survey.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations Machine Learning with a Reject Option: A survey

Reference 38

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This paper cites In: Artificial Intelligence in Medicine: 20th Inter- national Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, Proceedings, pp.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations In: Artificial Intelligence in Medicine: 20th Inter- national Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, Proceedings, pp

Reference 39

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This paper cites NPJ Digital Medicine4(1), 4 (2021) 20.

Clustered Calibration: Representation-Aware Probability Calibration via Learned Subpopulations NPJ Digital Medicine4(1), 4 (2021) 20

Reference 40

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Pith citing papers

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