Abstract
Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging. Current methods often struggle with scalability, limiting their applicability to large datasets, or make restrictive assumptions about feature-property relationships, hindering their ability to capture complex interactions. BoUTS's general and scalable feature selection algorithm surpasses these limitations by identifying both universal features relevant to all datasets and task-specific features predictive for specific subsets. Evaluated on seven diverse chemical regression datasets, BoUTS achieves state-of-the-art feature sparsity while generally maintaining prediction accuracy comparable to specialized methods. Notably, BoUTS's universal features enable domain-specific knowledge transfer between datasets, and we expect these results to be broadly useful to manually-guided inverse problems. Beyond its current application, BoUTS holds potential for elucidating data-poor systems by leveraging information from similar data-rich systems.Scientific Contribution: BoUTS selects nonlinear, universally informative features across multiple datasets. We identify crucial "universal features" across seven real-world chemistry datasets, which enhance cross-dataset interpretability and selection stability. BoUTS is highly scalable and is applicable to tabular data from many domains, and our results identify connections between seemingly unrelated chemical domains.