# Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets

arXiv: https://arxiv.org/abs/2403.14466 · HTML: https://mlsanity.com/paper/2403.14466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Gradient Boosted Feature Selection](https://mlsanity.com/paper/1901.04055/) (2019)
- [Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis](https://mlsanity.com/paper/1311.5422/) (2013)
- [Gradient Boosting Performs Gaussian Process Inference](https://mlsanity.com/paper/2206.05608/) (2022)
- [A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems](https://mlsanity.com/paper/1401.5136/) (2014)

## Built on by (most cited first, 1 of 1)

- [Joint Optimization of Piecewise Linear Ensembles](https://mlsanity.com/paper/2405.00303/) (2024)

## Abstract (arXiv)

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.