Mutually Adaptable Learning

Qi Tan, Yang Liu, Jiming Liu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


In real-world applications that involve complex data dependencies, it would be essential to proceed with machine learning tasks in an adaptable manner. This article presents a novel Mutually Adaptable Learning (MAL) approach that allows for, on the one hand, extracting the most crucial information from data, and on the other hand, maximally utilizing it through model learning, in a mutually adaptable manner. We elaborate our MAL approach by explaining how it determines the necessity for the adaptation of both features and the learning model, integratively adapts between feature selection and model learning, and optimally achieves the learning objective. To systematically validate the effectiveness of MAL, we conduct comprehensive experiments on challenging learning tasks from two representative domains: spatiotemporal prediction and chaotic behavioral prediction, where the complex data dependencies are general encountered. Results demonstrate that MAL outperforms existing learning methods. Moreover, we show that the formulated objective can be attained under an information-theoretic guarantee. With both empirical and theoretical supports, MAL offers an effective solution to the problem of feature and model adaptation to achieve desired learning objective for given complex tasks.
Original languageEnglish
Pages (from-to)240-254
Number of pages15
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number1
Early online date9 Aug 2023
Publication statusPublished - Feb 2024

Scopus Subject Areas

  • Computational Mathematics
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications

User-Defined Keywords

  • Complex data dependency
  • Mutually Adaptable Learning (MAL)
  • feature and model adaptation
  • information-theoretic analysis


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