What is it about?
The goal of automatic sequential decision-making is to learn a machine learning model in order to update knowledge online. However, learning a model from scratch can be time-consuming and inefficient. Consider an online recommendation system that is updated based on the sequence of clients. However, each client has only a few observations. How could we learn a reliable model quickly on a new client? Clearly, we could benefit from previous learned clients' information. In this paper, we present a novel machine learning model for quickly learning new tasks. Furthermore, each task is also learned sequentially, which saves memory and is more efficient.
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Why is it important?
Efficiently learning a new task with few observations is critical in many real-world scenarios. In this paper, we proposed various theoretical results as well as a novel practical framework for learning streaming data quickly and reliably. Furthermore, our model could track a non-stationary environment, which is both challenging and promising.
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This page is a summary of: Lifelong Online Learning from Accumulated Knowledge, ACM Transactions on Knowledge Discovery from Data, February 2023, ACM (Association for Computing Machinery), DOI: 10.1145/3563947.
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