Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings | Paper Citation Record

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings write newline

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Ensemble of averages: Improving model selection and boosting performance in domain generalization

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Unresolved cited work

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Hochberg, Y

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Estimating model performance under covariate shift without labels

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Nuanced metrics for measuring unintended bias with real data for text classification

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Domain generalization by mutual-information regularization with pre-trained models

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Detecting errors and estimating accuracy on unlabeled data with self-training ensembles

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings S., Poms, F., Fatahalian, K., and R \'e , C

Reference 9

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings An optimum character recognition system using decision functions

Reference 10

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Functional map of the world

Reference 11

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Estimating generalization under distribution shifts via domain-invariant representations

Reference 12

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings F., Williams, R., Reguera, B., Herry, V., and Gonz \'a lez-Gil, S

Reference 13

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings B., Lu, T., Luu, T., and P \'a l, D

Reference 14

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Zheng, L

Reference 15

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This paper cites What does rotation prediction tell us about classifier accuracy under varying testing environments? In International Conference on Machine Learning, pp.\ 2579--2589.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings What does rotation prediction tell us about classifier accuracy under varying testing environments? In International Conference on Machine Learning, pp.\ 2579--2589

Reference 16

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This paper cites Confidence and dispersity speak: Characterizing prediction matrix for unsupervised accuracy estimation.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Confidence and dispersity speak: Characterizing prediction matrix for unsupervised accuracy estimation

Reference 17

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Unsupervised supervised learning i: Estimating classification and regression errors without labels

Reference 18

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Unresolved cited work

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Gall \'e , M

Reference 20

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Davidson, I

Reference 21

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Selective prediction-set models with coverage rate guarantees

Reference 22

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Ghahramani, Z

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Selective classification via one-sided prediction

Reference 24

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This paper cites Leveraging Unlabeled Data to Predict Out-of-Distribution Performance.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and El-Yaniv, R

Reference 26

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and El-Yaniv, R

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Bias-reduced uncertainty estimation for deep neural classifiers

Reference 28

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings A Learning Based Hypothesis Test for Harmful Covariate Shift

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Yuan, X

Reference 31

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Predicting with confidence on unseen distributions

Reference 32

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Lopez-Paz, D

Reference 33

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Unresolved cited work

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Deep residual learning for image recognition

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Multicalibration: Calibration for the (computationally-identifiable) masses

Reference 36

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Dietterich, T

Reference 37

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Aries: Efficient testing of deep neural networks via labeling-free accuracy estimation

Reference 39

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Self-adaptive training: beyond empirical risk minimization

Reference 41

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Estimating the accuracies of multiple classifiers without labeled data

Reference 42

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Assessing Generalization of SGD via Disagreement

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Adam: A Method for Stochastic Optimization

Reference 44

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Confidence-based Estimators for Predictive Performance in Model Monitoring

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings W., Sagawa, S., Marklund, H., Xie, S

Reference 46

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Fairness in credit scoring: Assessment, implementation and profit implications

Reference 47

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Inouye, D

Reference 48

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Simple and scalable predictive uncertainty estimation using deep ensembles

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings L., Romano, J

Reference 50

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Learning a data-driven policy network for pre-training automated feature engineering

Reference 51

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings P., Salakhutdinov, R

Reference 52

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Decoupled Weight Decay Regularization

Reference 53

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Predicting Out-of-Distribution Error with Confidence Optimal Transport

Reference 54

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Co-validation: Using model disagreement on unlabeled data to validate classification algorithms

Reference 55

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Performance prediction under dataset shift

Reference 56

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This paper cites Dataset Inference: Ownership Resolution in Machine Learning.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Dataset Inference: Ownership Resolution in Machine Learning

Reference 57

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings K-means clustering based feature consistency alignment for label-free model evaluation

Reference 58

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Reference 59

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This paper cites Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift

Reference 60

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Came: Contrastive automated model evaluation

Reference 61

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This paper cites Energy-based Automated Model Evaluation.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Energy-based Automated Model Evaluation

Reference 62

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings M., and Horvitz, E

Reference 63

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings A., Dubey, A., and Mitchell, T

Reference 64

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Reference 66

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Tracking the risk of a deployed model and detecting harmful distribution shifts

Reference 67

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Reference 68

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Failing loudly: An empirical study of methods for detecting dataset shift

Reference 69

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This paper cites Selective Prediction via Training Dynamics.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Selective Prediction via Training Dynamics

Reference 70

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Learning to validate the predictions of black box machine learning models on unseen data

Reference 71

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Imagenet large scale visual recognition challenge

Reference 72

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 73

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Learning to validate the predictions of black box classifiers on unseen data

Reference 74

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Observation c8e7c8f2-717e-4344-b9e7-01602dd4679a · outbound

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Label-Free Model Evaluation with Semi-Structured Dataset Representations

Reference 75

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Observation 18a810bb-2029-495f-afcc-d589e4ed3a4f · outbound

This paper cites Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Target specification bias, counterfactual prediction, and algorithmic fairness in healthcare

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:20:30.639259Z

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Observation 71b0a790-df30-4a82-bec4-012d86d228a4 · outbound

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Rxrx1: An image set for cellular morphological variation across many experimental batches

Reference 77

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings V., Pacchioni, F., Bellumore, S., Bosia, M., and De Micco, F

Reference 78

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings A bag-of-prototypes representation for dataset-level applications

Reference 79

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Predicting Neural Network Accuracy from Weights

Reference 80

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings and Nowacki, A

Reference 81

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Reference 82

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Toward auto-evaluation with confidence-based category relation-aware regression

Reference 83

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Reference 84

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Testing statistical hypotheses of equivalence

Reference 85

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings A Fine-Grained Analysis on Distribution Shift

Reference 86

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings On the importance of feature separability in predicting out-of-distribution error

Reference 87

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Observation 40253399-420e-4481-9157-6756f9892f78 · outbound

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Predicting out-of-distribution error with the projection norm

Reference 88

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Observation e6c736bd-38ed-473a-9b82-3e7e1e082ae3 · outbound

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Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Domain generalization with mixstyle

Reference 89

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Reference 90

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Pith citing papers