Unknown Examples & Machine Learning Model Generalization

24 Aug 2018Yeounoh ChungPeter J. HaasEli UpfalTim Kraska

Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential training examples are unknown to the modeler, due to sample selection bias or, more generally, covariate shift, i.e., a distribution shift between the training and deployment stage... (read more)

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