The recent developments in the research area have seen significant advancements in interpretable machine learning and efficient estimation techniques. Notably, there is a growing focus on developing novel algorithms that not only improve computational efficiency but also enhance the robustness and interpretability of models. For instance, the introduction of Kernel Banzhaf offers a robust alternative to Shapley values, leveraging linear regression for efficient feature attribution. Similarly, Conditional Density Tree (CDTree) models have been proposed for conditional density estimation, providing a more interpretable and accurate approach compared to existing methods. In the realm of automata theory, new constructions for complementing Emerson-Lei automata have been developed, offering exponential improvements in computational bounds. Additionally, advancements in tree-based estimators, such as FastPD, have been made to efficiently estimate partial dependence functions, addressing the limitations of existing methods like TreeSHAP. These innovations collectively push the boundaries of current methodologies, making significant strides in both theoretical and practical applications.