The field of machine learning and information theory is moving towards the development of more efficient and effective methods for data analysis and processing. One of the key directions is the use of machine learning techniques to improve the performance of traditional data structures and algorithms. Additionally, there is a growing interest in the development of new evaluation measures and metrics that can better capture the complexity and nuances of real-world data. The use of entropy and information theory concepts is also becoming increasingly popular in the development of new methods and techniques. Notably, the introduction of new informational functionals and measures of statistical complexity is providing new insights and tools for data analysis. Overall, the field is experiencing a significant shift towards more sophisticated and powerful methods for data analysis and processing.

Noteworthy papers include: The paper on learned static function data structures, which introduces a new approach to constructing data structures using machine learning techniques, resulting in significant space savings. The paper on the Eigenvalues Entropy as a Classifier Evaluation Measure, which proposes a new evaluation measure for classification methods that is more accurate for imbalanced datasets.