Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
<|reference_start|>Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets: 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 to identify 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 maintaining prediction accuracy comparable to specialized methods. Notably, BoUTS's universal features enable domain-specific knowledge transfer between datasets, and suggest deep connections in seemingly-disparate chemical datasets. We expect these results to have important repercussions in manually-guided inverse problems. Beyond its current application, BoUTS holds immense potential for elucidating data-poor systems by leveraging information from similar data-rich systems. BoUTS represents a significant leap in cross-domain feature selection, potentially leading to advancements in various scientific fields.<|reference_end|>
@article{raymond2024universal,
    title={Universal Feature Selection for Simultaneous Interpretability of
  Multitask Datasets},
    author={Matt Raymond, Jacob Charles Saldinger, Paolo Elvati, Clayton Scott,
  Angela Violi},
    journal={arXiv preprint arXiv:2403.14466},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14466},
    primaryClass={cs.LG}
}