Interpretable machine learning for Kronecker coefficients
Giorgi Butbaia, Kyu-Hwan Lee, Fabian Ruehle · Advances in Theoretical and Mathematical Physics · 2026
We analyze the saliency of neural networks and employ interpretable machine learning models to predict whether the Kronecker coefficients of the symmetric group are zero or not. Our models use triples of partitions as input features, as well as $b$-loadings derived from the principal component of an embedding that captures the differences between partitions. Across all approaches, we achieve an accuracy of approximately 83% and derive explicit formulas for a decision function in terms of $b$-loadings. Additionally, we develop transformer-based models for prediction, achieving the highest reported accuracy of over 99%.