Learning the importance of training data under concept drift

Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research

The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal data with the hope that the training data used will accurately represent inputs the model may receive in the future. More generally, the default assumption that all training data are equally relevant often breaks in practice. For example, the figure below shows …

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