Mutually Adaptable Learning

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 allows for, on the one hand, extracting most crucial information from data, and other maximally utilizing through model learning, mutually We elaborate our MAL by explaining how determines necessity for adaptation of both features model, integratively adapts between feature selection optimally achieves objective. To systematically validate effectiveness MAL, we conduct comprehensive experiments challenging two representative domains: spatiotemporal prediction chaotic behavioral prediction, where dependencies are general encountered. Results demonstrate outperforms existing methods. Moreover, show formulated objective can attained under information-theoretic guarantee. With empirical theoretical supports, offers effective solution problem achieve desired given tasks.