Machine Learning under Changing Environments

现有的机器学习研究已经基于数据生成环境不随时间变化的假设开发了理论和算法。然而，最近的应用程序不满足这样的平稳性假设。因此，在这个项目中，我们开发了应对不断变化的环境的基本理论和实用算法。我们进一步将开发的算法应用于各种现实世界的应用，包括机器人，图像识别，脑信号分析，语音识别和自然语言处理。

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.