Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift | Paper Citation Record

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This paper cites write newline.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift write newline

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This paper cites M., Toni, L., and Rodrigues, M.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift M., Toni, L., and Rodrigues, M

Reference 2

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This paper cites N., Bates, S., Fannjiang, C., Jordan, M.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift N., Bates, S., Fannjiang, C., Jordan, M

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift PPI++: Efficient Prediction-Powered Inference

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift W., and Kang, H

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Using Experiments to Correct for Selection in Observational Studies

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift D., Sklar, M., Berk, R., Buja, A., and Zhao, L

Reference 7

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Dual and anti-dual modes in dielectric spheres

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This paper cites J., Klaassen, J., Ritov, Y., and Wellner, J.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift J., Klaassen, J., Ritov, Y., and Wellner, J

Reference 9

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This paper cites Models as approximations i: consequences illustrated with linear regression.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Models as approximations i: consequences illustrated with linear regression

Reference 10

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Unresolved cited work

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Guo, Z

Reference 12

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift J., Ruppert, D., Stefanski, L

Reference 13

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Cai, T

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Semi-supervised learning

Reference 15

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and White, H

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift N., and Cai, T

Reference 17

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning

Reference 18

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Applied Causal Inference Powered by ML and AI

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This paper cites A Computational Approach to Politeness with Application to Social Factors.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift A Computational Approach to Politeness with Application to Social Factors

Reference 20

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Optimal and safe estimation for high-dimensional semi-supervised learning

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Kennedy, E

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift On the Role of Surrogates in Conformal Inference of Individual Causal Effects

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift A unified view of label shift estimation

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Can Unconfident LLM Annotations Be Used for Confident Conclusions?

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Covariate shift by kernel mean matching

Reference 28

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift R., and Cheng, D

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Label correction of crowdsourced noisy annotations with an instance-dependent noise transition model

Reference 30

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Top challenges from the first practical online controlled experiments summit

Reference 31

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Demystifying statistical learning based on efficient influence functions

Reference 32

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Surrogate assisted semi-supervised inference for high dimensional risk prediction

Reference 33

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Correcting sample selection bias by unlabeled data

Reference 34

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Newey, W

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Long-term causal inference under persistent confounding via data combination

Reference 36

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Maximum mean discrepancy for class ratio estimation: Convergence bounds and kernel selection

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Mao, X

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Retasa: A nonparametric functional estimation approach for addressing continuous target shift

Reference 42

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Semi-supervised regression: A recent review

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Unresolved cited work

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Martinet, G

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Sch \"o lkopf, B

Reference 46

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Doubly flexible estimation under label shift

Reference 47

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift T., Bost, S., Lyu, T., Wu, Y., Hogan, W., Prosperi, M., et al

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Provably end-to-end label-noise learning without anchor points

Reference 49

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Detecting and correcting for label shift with black box predictors

Reference 50

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Identifiability of label noise transition matrix

Reference 51

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Improved estimators for semi-supervised high-dimensional regression model

Reference 52

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Assumption-Lean and Data-Adaptive Post-Prediction Inference

Reference 53

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Valid inference for machine learning-assisted genome-wide association studies

Reference 54

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This paper cites and Witten, D.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Witten, D

Reference 55

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift D., Christoffel, M., and Sugiyama, M

Reference 56

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Reference 57

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Reference 58

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This paper cites Generalization error bounds in semi-supervised classification under the cluster assumption.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Generalization error bounds in semi-supervised classification under the cluster assumption

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:06.533732Z

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Observation 6fcddadc-10b5-4191-88aa-47bcfd93c3ad · outbound

This paper cites In all likelihoods: Robust selection of pseudo-labeled data.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift In all likelihoods: Robust selection of pseudo-labeled data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:06.305642Z

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This paper cites On causal and anticausal learning.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift On causal and anticausal learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:06.138288Z

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Observation 0814729a-2a66-4d88-a29d-972de488cc32 · outbound

This paper cites A rate of convergence for mixture proportion estimation, with application to learning from noisy labels.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift A rate of convergence for mixture proportion estimation, with application to learning from noisy labels

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:05.891635Z

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Observation 3bf69dd6-fbb8-478c-9d1d-40718a07d29b · outbound

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Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Improving predictive inference under covariate shift by weighting the log-likelihood function

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:05.675406Z

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Observation be8b39ef-b456-4222-aacc-d3b71057635d · outbound

This paper cites A general m-estimation theory in semi-supervised framework.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift A general m-estimation theory in semi-supervised framework

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:05.491797Z

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Observation 1ba23859-04a7-4562-b850-3ad7c4cb6bb9 · outbound

This paper cites and Cowie, C.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Cowie, C

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:05.261376Z

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This paper cites When training and test sets are different: characterizing learning transfer.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift When training and test sets are different: characterizing learning transfer

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:05.038205Z

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This paper cites and Kawanabe, M.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Kawanabe, M

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:04.820024Z

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Observation 2eb5083a-b75a-419d-a067-94491b81dc9b · outbound

This paper cites Direct importance estimation for covariate shift adaptation.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Direct importance estimation for covariate shift adaptation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:04.527737Z

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Observation a18133ea-ea1d-47d9-8a5d-373385f2c7fc · outbound

This paper cites Fisher consistency for prior probability shift.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Fisher consistency for prior probability shift

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:04.359187Z

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This paper cites Elsa: Efficient label shift adaptation through the lens of semiparametric models.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Elsa: Efficient label shift adaptation through the lens of semiparametric models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:04.173028Z

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Observation a6585666-25e6-4127-b09f-9be0c6a6747a · outbound

This paper cites Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments

Reference 71

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Observation 96a440a4-b54a-4056-b800-51fa2a5695a0 · outbound

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Reference 72

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Observation df33d49d-1ce9-4889-b29f-8a83b14c858e · outbound

This paper cites an unresolved cited work.

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Reference 73

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Observation 35fcd89b-d472-43b4-af08-c17d160a6f13 · outbound

This paper cites J., Polley, E.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift J., Polley, E

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:03.541584Z

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Reference 75

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Observation 19d2bd4e-cae0-49ae-bb44-2394af21b441 · outbound

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

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Adaptive Concentration of Regression Trees, with Application to Random Forests

Reference 76

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This paper cites H., and Leek, J.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift H., and Leek, J

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:03.058432Z

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Observation b17fd698-2ba3-4236-9e19-6433a0eb8437 · outbound

This paper cites Usb: A unified semi-supervised learning benchmark for classification.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Usb: A unified semi-supervised learning benchmark for classification

Reference 78

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Observation a2391b80-e3de-4969-ae9f-ed594ba49a1b · outbound

This paper cites and Lafferty, J.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Lafferty, J

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:02.707271Z

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Observation 50ab7d85-b0aa-42e7-956f-266676135696 · outbound

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Reference 80

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Reference 81

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Observation e40f7f6a-8843-409f-9105-7b382974d7f8 · outbound

This paper cites D., and Cai, T.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift D., and Cai, T

Reference 82

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This paper cites Domain adaptation under target and conditional shift.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Domain adaptation under target and conditional shift

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:01.983615Z

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This paper cites Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix

Reference 84

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This paper cites and Bradic, J.

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift and Bradic, J

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:12:01.731510Z

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Observation d58ae6fc-be8f-43e1-af93-c92c6e37dfcd · outbound

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Reference 86

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