Dzung Phan, Vinicius Lima
INFORMS 2023
Different researchers hold different views of what the term meta-learning exactly means. The first part of this paper provides our own perspective view in which the goal is to build self-adaptive learners (i.e. learning algorithms that improve their bias dynamically through experience by accumulating meta-knowledge). The second part provides a survey of meta-learning as reported by the machine-learning literature. We find that, despite different views and research lines, a question remains constant: how can we exploit knowledge about learning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearly the answer to this question is key to the advancement of the field and continues being the subject of intensive research.
Dzung Phan, Vinicius Lima
INFORMS 2023
Ella Barkan, Ibrahim Siddiqui, et al.
Computational And Structural Biotechnology Journal
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008
Gang Liu, Michael Sun, et al.
ICLR 2025