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artifacts
artifacts
 
 
black-box-models
black-box-models
 
 
datasets
datasets
 
 
pp
pp
 
 
.gitignore
.gitignore
 
 
LICENSE.md
LICENSE.md
 
 
README.md
README.md
 
 
example.py
example.py
 
 
experiment_autokeras.py
experiment_autokeras.py
 
 
experiment_autosklearn.py
experiment_autosklearn.py
 
 
experiment_calibration.py
experiment_calibration.py
 
 
experiment_tpot.py
experiment_tpot.py
 
 
experiment_unknownshift.py
experiment_unknownshift.py
 
 
experiments__correct_shift__adversarial.py
experiments__correct_shift__adversarial.py
 
 
experiments__correct_shift__missing.py
experiments__correct_shift__missing.py
 
 
experiments__correct_shift__noisy.py
experiments__correct_shift__noisy.py
 
 
experiments__correct_shift__outliers.py
experiments__correct_shift__outliers.py
 
 
experiments__correct_shift__rotation.py
experiments__correct_shift__rotation.py
 
 
experiments__correct_shift__scaling.py
experiments__correct_shift__scaling.py
 
 
experiments__correct_shift__swapped.py
experiments__correct_shift__swapped.py
 
 
experiments_all_shifts.py
experiments_all_shifts.py
 
 
experiments_google_cloud.py
experiments_google_cloud.py
 
 
experiments_google_cloud_heart.py
experiments_google_cloud_heart.py
 
 
experiments_lasso_predict.py
experiments_lasso_predict.py
 
 
experiments_lasso_train.py
experiments_lasso_train.py
 
 
requirements.txt
requirements.txt
 
 
revision__calibration.py
revision__calibration.py
 
 
revision__dtest_variation.py
revision__dtest_variation.py
 
 
revision__noise_on_features_missing.py
revision__noise_on_features_missing.py
 
 
revision__noise_on_features_missing_noiseeval.py
revision__noise_on_features_missing_noiseeval.py
 
 
revision__noise_on_features_swapped.py
revision__noise_on_features_swapped.py
 
 
revision__slices_adult.py
revision__slices_adult.py
 
 
revision__taxi-example-nodomain.py
revision__taxi-example-nodomain.py
 
 
revision__taxi-example.py
revision__taxi-example.py
 
 
revision_largeconvnet.py
revision_largeconvnet.py
 
 
revision_slices_heart.py
revision_slices_heart.py
 
 
train_black_box_model.py
train_black_box_model.py
 
 

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projects-black-box-performance-prediction

Code for our SIGMOD paper on learning to predict the performance of black box classifiers on unseen data.

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Code and experiments for our SIGMOD paper on "Learning to Validate the Predictions of Black Box Classifiers on Unseen Data"

ssc.io/publication/learning-to-validate-the-predictions-of-black-box-classifiers-on-unseen-data-sigmod/

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