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With the increasing adoption of machine learning (ML) models and systems in high-stakes settings across different industries, guaranteeing a model's performance after deployment has become crucial.
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Tsymbal, A · 2004
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Murphy, K. P · 2012
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A · 2014
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Doubly robust covariate shift correction
Reddi, S., Poczos, B., and Smola, A · 2015
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Hidden technical debt in machine learning systems
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Robust random cut forest based anomaly detection on streams
Guha, S., Mishra, N., Roy, G., and Schrijvers, O · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Optimal quantile approximation in streams
Karnin, Z., Lang, K., and Liberty, E · 2016
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“Why should I trust you?” Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Dua, D., and Graff, C · 2017
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Lundberg, S. M., and Lee, S.-I · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Detecting and correcting for label shift with black box predictors
Lipton, Z., Wang, Y.-X., and Smola, A · 2018
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Relative error streaming quantiles
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Fairness measures for machine learning in finance
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