We propose a new form of predictive models, Partially Interpretable Estimators (PIE), which jointly train an interpretable model and a black-box model to achieve partial model transparency while maintaining high predictive performance. Our design is motivated by prior research showing that interpretability does not require exposing all model details. Therefore, our objective is to explain the main components of the prediction, withholding complicated calculations that may not be necessary for users. PIE is designed to attribute a prediction to the contribution from individual features via a sparse linear additive model to achieve interpretability while complementing the prediction with a black-box model to boost the predictive performance. As such, the linear additive model captures the primary feature contributions, while the black-box component augments PIE's predictive power by capturing the “nuances” of feature interactions as a refinement. Moreover, we include a sparsity constraint, allowing users to adjust the model to meet domain-specific needs of interpretability. To optimize predictive performance, we propose a coordinated training algorithm that jointly trains the two components of PIE. Experimental results show that PIE achieves accuracy comparable to state-of-the-art black-box models, with human assessments confirming that its interpretability is nearly equivalent to linear models.

Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal definition across contexts such as art, scientific research, and games. Existing approaches to creativity often focus on proposing informal conceptual definitions without providing formal computational concepts. Alternatively, they propose formal definitions without carefully examining their underlying assumptions or providing validation of these definitions by testing their performance in specific application settings. To address these problems, this paper proposes several formal postulates of creativity grounded in prior psychological work on creativity, especially as related to Boden's three core criteria of creativity: newness, value, and surprise. Furthermore, we propose a conceptual mathematical definition of creativity that aligns with these postulates. We also validate our proposed creativity formula in two applications: determining the creativity of particular chess moves, and assessing the creativity of paintings by some artists, such as Picasso, Renoir, and other painters. Experimental results demonstrate that our formula of creativity successfully captures creative patterns across both the chess and the art domains. This suggests that creativity can be effectively characterized through a unified mathematical structure based on three fundamental criteria, namely newness, value, and surprise, whose specific measurements are adapted to the particular domains.

Prediction problems in Machine Learning can be divided into three major categories: easy, difficult and “next to impossible”, depending on the difficulty of accurate predictions based on the features extracted from classified objects. For example, it is easy to extract a set of features from a photo to differentiate cat-vs-dog images using certain intrinsic characteristics of the corresponding classes, while identifying pianist tradition (e.g., German vs. Russian) from a sonata performance is next-to-impossible due to nuanced implicit characteristics that defy proper characterization. Professional pianists can do it fairly accurately, but their explanations use terms like “colorfully” or “philosophically,” making it challenging to define explicit tangible features. Our paper focuses on such “next to impossible” problems, mainly from the music domain. We aim to identify a finite set of explicit features for accurate classifications. We apply a leading AutoML system to piano music recordings for Pianist School Classification (German vs. Russian Schools) and Music Genre Recognition tasks.

Surprisingly, AutoML performed well on these “super-hard” classification tasks, competing with some of the leading Deep Learning methods despite their several advantages. The key to AutoML's success on such “next-to-impossible” classification tasks lies in its ability to generate a vast set of all possible features (283 features in our case) extracted by domain experts from the classification objects, on which AutoML “unleashed” its model-generating power. This result provides extra evidence of the AutoML's ability to solve next-to-impossible classification tasks. Although we have not proven that the explicit feature-based ML methods can solve any classification task, no matter how hard it is, our work provides additional evidence that this is indeed the case.

Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL paradigms face challenges due to heterogeneity in client data distributions and system capabilities. Personalized federated learning (pFL) has been proposed to mitigate these problems, but often requires a shared model architecture and a central entity for parameter aggregation, resulting in scalability and communication issues. More recently, model-heterogeneous FL has gained attention due to its ability to support diverse client models, but existing methods are limited by their dependence on a centralized framework, synchronized training, and publicly available datasets. To address these limitations, we introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information. Experimental results show that FedPAE outperforms existing state-of-the-art pFL algorithms, effectively managing diverse client capabilities and demonstrating robustness against statistical heterogeneity.