Machine Learning under Changing Environments

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Existing machine learning research has developed theories and algorithms based on the assumption that data generating environments do not change over time. However, recent applications do not satisfy such a stationarity assumption. In this project, we therefore developed fundamental theories and practical algorithms for coping with changing environments. We further applied the developed algorithms to various real-world applications including robotics, image recognition, brain signal analysis, speech recognition, and natural language processing.