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| Research Advances on Theory of Open-Environment Machine Learning |
| YUAN Xiaotong1, ZHANG Xuyao2,3, LIU Xi1, CHENG Zhen2,3, LIU Chenglin2,3 |
1. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044;
2. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190;
3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049 |
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Abstract In an open environment, machine learning is faced with various challenges, including varying category sets, non-identically distributed data and noise interference. These challenges can result in a significant decline in the performance of traditional machine learning systems built under the closed-world assumption. Therefore, open-environment machine learning is a research focus on artificial intelligence. In this paper, the current status and recent important advances in the theoretical study of open-environment machine learning are discussed from the perspectives of generalization, optimization, robustness and performance measurement. For generalization theory, the advances on the generalization performance analysis of open-set detection, transfer/meta learning and sparse learning approaches are introduced. For optimization theory, the advances on the theoretical analysis of random and sparse optimization, online and continual optimization, as well as distributed and federated optimization approaches are introduced. For robustness theory, the advances on robust learning under adversarial samples, random noise and noisy labels are introduced. For performance measurement, a number of widely used performance measurement criterions for open-environment machine learning are introduced. Finally, some prospects on the theoretical research trends of open-environment machine learning are provided.
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Received: 09 October 2023
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| Fund:Supported by National Key Research and Development Program of China(No.2018AAA0100400); National Natural Science Foundation of China(No.U21B2049); National Natural Science Foundation of China(No.61936005); National Natural Science Foundation of China(No.62222609); National Natural Science Foundation of China(No.62076236) |
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Corresponding Authors:
YUAN Xiaotong, Ph.D., professor. His research interests include machine learning, stochastic optimization and computer vision.
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About author:: ZHANG Xuyao, Ph.D., professor. His research interests include pattern recognition, machine learning and deep learning.
LIU Xi, master student. Her research inte-rests include federated learning, transfer learning and pattern recognition.
CHENG Zhen, Ph.D. candidate. His research interests include pattern recognition, machine learning and adversarial robustness.
LIU Chenglin, Ph.D., professor. His research interests include pattern recognition, machine learning, and document analysis and recognition. |
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2023, Vol. 36 
