"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift | Paper Citation Record

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This paper cites The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting

Reference 1

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This paper cites Generalized random forests.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Generalized random forests

Reference 2

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 3

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This paper cites and Chernozhukov, V.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift and Chernozhukov, V

Reference 4

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This paper cites Why did the distribution change? In Banerjee, A.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Why did the distribution change? In Banerjee, A

Reference 5

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This paper cites Diagnosing Model Performance Under Distribution Shift.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Diagnosing Model Performance Under Distribution Shift

Reference 6

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift C., Walker, I., and Glocker, B

Reference 7

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This paper cites Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors

Reference 8

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This paper cites Double/debiased machine learning for treatment and structural parameters.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Double/debiased machine learning for treatment and structural parameters

Reference 9

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This paper cites Monitor alarm fatigue: an integrative review.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Monitor alarm fatigue: an integrative review

Reference 10

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This paper cites R., and Leyton-Brown, K.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift R., and Leyton-Brown, K

Reference 11

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This paper cites Retiring adult: New datasets for fair machine learning.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Retiring adult: New datasets for fair machine learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:39.053372Z

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Observation 6b38cdcf-b41c-4342-9a33-1c7037f65e79 · outbound

This paper cites Efficiently mitigating the impact of data drift on machine learning pipelines.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Efficiently mitigating the impact of data drift on machine learning pipelines

Reference 13

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This paper cites K., Delbrouck, J.-B., Lee-Messer, C., Dunnmon, J., Zou, J., and Re, C.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift K., Delbrouck, J.-B., Lee-Messer, C., Dunnmon, J., Zou, J., and Re, C

Reference 14

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This paper cites Is this model reliable for everyone? testing for strong calibration.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Is this model reliable for everyone? testing for strong calibration

Reference 15

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 16

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This paper cites Not all clinical AI monitoring systems are created equal: Review and recommendations.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Not all clinical AI monitoring systems are created equal: Review and recommendations

Reference 17

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This paper cites G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I

Reference 18

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 19

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This paper cites An algorithmic framework for bias bounties.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift An algorithmic framework for bias bounties

Reference 20

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This paper cites M., Rasch, M.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift M., Rasch, M

Reference 21

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This paper cites M., Rasch, M.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift M., Rasch, M

Reference 22

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This paper cites Multicalibration: Calibration for the ( C omputationally-identifiable) masses.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Multicalibration: Calibration for the ( C omputationally-identifiable) masses

Reference 23

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This paper cites Diagnostic Runtime Monitoring with Martingales.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Diagnostic Runtime Monitoring with Martingales

Reference 24

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This paper cites Variable importance measures for heterogeneous causal effects, 2023.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Variable importance measures for heterogeneous causal effects, 2023

Reference 25

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This paper cites Consistent tests for conditional treatment effects.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Consistent tests for conditional treatment effects

Reference 26

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This paper cites Nonparametric inference on non-negative dissimilarity measures at the boundary of the parameter space.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Nonparametric inference on non-negative dissimilarity measures at the boundary of the parameter space

Reference 27

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 28

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 29

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This paper cites P., Ghorbani, A., and Zou, J.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift P., Ghorbani, A., and Zou, J

Reference 30

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift W., Nguyen, T., Tang, Y

Reference 31

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift W., Sagawa, S., Marklund, H., Xie, S

Reference 32

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Observation afb61e5f-c55c-4da2-8129-b12fbbc8fbfd · outbound

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 33

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift H., and Leskovec, J

Reference 34

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This paper cites On the need for a language describing distribution shifts: Illustrations on tabular datasets.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift On the need for a language describing distribution shifts: Illustrations on tabular datasets

Reference 35

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This paper cites W., Moerkerke, B., Loeys, T., and Vansteelandt, S.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift W., Moerkerke, B., Loeys, T., and Vansteelandt, S

Reference 36

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 37

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Observation dd8702df-428f-4080-b266-bd871cedf248 · outbound

This paper cites Algorithmic fairness: Choices, assumptions, and definitions.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Algorithmic fairness: Choices, assumptions, and definitions

Reference 38

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This paper cites GPT-4o System Card.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift GPT-4o System Card

Reference 39

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift S., Balasubramanian, V

Reference 40

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Scikit-learn: Machine learning in P ython

Reference 41

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift and Ramdas, A

Reference 42

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Observation 80ed6f8c-7291-4a1e-973f-40d0a8b75a8a · outbound

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 43

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Observation 6cea681a-7268-4435-adac-18ed4dcdb1d1 · outbound

This paper cites T., Santiago, E., Mu, J., and Heckerman, D.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift T., Santiago, E., Mu, J., and Heckerman, D

Reference 44

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Observation 941026bc-99bb-4f49-8e40-f5d4fef89302 · outbound

This paper cites R., and Bauer, S.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift R., and Bauer, S

Reference 45

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Observation 6d91ac8b-9fe9-4ec8-bb75-ce99b748408f · outbound

This paper cites Failing loudly: An empirical study of methods for detecting dataset shift.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Failing loudly: An empirical study of methods for detecting dataset shift

Reference 46

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Observation c640293c-e454-40bb-b625-f1308a649341 · outbound

This paper cites R., and van der Schaar, M.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift R., and van der Schaar, M

Reference 47

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Observation b2a3ced1-6f20-4691-8807-fcabce4daf29 · outbound

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"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 48

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Observation cd20f6e3-4c19-47fa-b78c-2dd2c41c505e · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 49

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Observation 223eb029-f605-4834-8833-12100a522c0f · outbound

This paper cites A hierarchical decomposition for explaining ML performance discrepancies.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift A hierarchical decomposition for explaining ML performance discrepancies

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.731636Z

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Observation 8779275d-b972-4878-9348-757774ac14f2 · outbound

This paper cites An empirical analysis of backward compatibility in machine learning systems.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift An empirical analysis of backward compatibility in machine learning systems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.717997Z

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Observation 5f435f24-de25-40af-b47b-6bd78a640730 · outbound

This paper cites an unresolved cited work.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:04:37.703693Z

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Observation b9f92c22-3d1d-4acd-a4fb-ba3a390e7994 · outbound

This paper cites C., and Saria, S.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift C., and Saria, S

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.687948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f87edb6a-1f55-41d1-99ed-0d8dd15317d4 · outbound

This paper cites Direct importance estimation with model selection and its application to covariate shift adaptation.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Direct importance estimation with model selection and its application to covariate shift adaptation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.672669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9038bbcf-2c22-44b5-9f9e-92330e3b7718 · outbound

This paper cites M., Ghassemi, M., and Ustun, B.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift M., Ghassemi, M., and Ustun, B

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.657983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 923add8b-3009-444e-a43c-a4623e87465e · outbound

This paper cites an unresolved cited work.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:04:37.643739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b32dd548-9a56-432d-846d-84c22ef52814 · outbound

This paper cites Adaptive Concentration of Regression Trees, with Application to Random Forests.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Adaptive Concentration of Regression Trees, with Application to Random Forests

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:04:35.548825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe60bc6-3d3d-4052-b613-5d6487e3d9b0 · outbound

This paper cites D., Gilbert, P.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift D., Gilbert, P

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:04:35.615850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c8baff6-9dc9-4fa1-8108-e0c61083cf7a · outbound

This paper cites Explaining medical AI performance disparities across sites with confounder Shapley value analysis.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Explaining medical AI performance disparities across sites with confounder Shapley value analysis

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:04:36.567635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6eaef078-1fc2-4389-a073-9caad6467f71 · outbound

This paper cites Change is hard: A closer look at subpopulation shift.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Change is hard: A closer look at subpopulation shift

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.625597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fa1efe34-593d-4fe1-a9e7-cf0fd609039b · outbound

This paper cites v., Hinz, O., and Feuerriegel, S.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift v., Hinz, O., and Feuerriegel, S

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:04:35.824299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4de395a7-b320-412f-b347-0d535f711184 · outbound

This paper cites `` W hy did the model fail?'': Attributing model performance changes to distribution shifts.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift `` W hy did the model fail?'': Attributing model performance changes to distribution shifts

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:37.585345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac2f9b61-37b0-485d-a214-7624aba6cb3d · outbound

This paper cites Kernel-based conditional independence test and application in causal discovery.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Kernel-based conditional independence test and application in causal discovery

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T12:04:35.953197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers