# Safe Screening Rules for Group OWL Models

arXiv: https://arxiv.org/abs/2504.03152 · HTML: https://mlsanity.com/paper/2504.03152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [The Benefit of Multitask Representation Learning](https://mlsanity.com/paper/1505.06279/) (2015)
- [Gap Safe screening rules for sparsity enforcing penalties](https://mlsanity.com/paper/1611.05780/) (2016)
- [SLOPE - Adaptive variable selection via convex optimization](https://mlsanity.com/paper/1407.3824/) (2014)
- [Mind the duality gap: safer rules for the Lasso](https://mlsanity.com/paper/1505.03410/) (2015)
- [Fast OSCAR and OWL Regression via Safe Screening Rules](https://mlsanity.com/paper/2006.16433/) (2020)
- [Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling](https://mlsanity.com/paper/1602.02485/) (2016)
- [GAP Safe screening rules for sparse multi-task and multi-class models](https://mlsanity.com/paper/1506.03736/) (2015)
- [Screening Tests for Lasso Problems](https://mlsanity.com/paper/1405.4897/) (2014)
- [An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification](https://mlsanity.com/paper/2208.06058/) (2022)
- [Group SLOPE - adaptive selection of groups of predictors](https://mlsanity.com/paper/1610.04960/) (2016)
- [Screening Rules for Lasso with Non-Convex Sparse Regularizers](https://mlsanity.com/paper/1902.06125/) (2019)
- [GAP Safe Screening Rules for Sparse-Group-Lasso](https://mlsanity.com/paper/1602.06225/) (2016)
- [Safe Screening for Multi-Task Feature Learning with Multiple Data Matrices](https://mlsanity.com/paper/1505.04073/) (2015)
- [Group-sparse Matrix Recovery](https://mlsanity.com/paper/1402.5077/) (2014)

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

- [Safe Screening Rules for Group SLOPE](https://mlsanity.com/paper/2506.09451/) (2025)

## Abstract (arXiv)

Group Ordered Weighted L1-Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learning with correlated features. Proximal gradient methods are used as standard approaches to solving Group OWL models. However, Group OWL models usually suffer huge computational costs and memory usage when the feature size is large in the high-dimensional scenario. To address this challenge, in this paper, we are the first to propose the safe screening rule for Group OWL models by effectively tackling the structured non-separable penalty, which can quickly identify the inactive features that have zero coefficients across all the tasks. Thus, by removing the inactive features during the training process, we may achieve substantial computational gain and memory savings. More importantly, the proposed screening rule can be directly integrated with the existing solvers both in the batch and stochastic settings. Theoretically, we prove our screening rule is safe and also can be safely applied to the existing iterative optimization algorithms. Our experimental results demonstrate that our screening rule can effectively identify the inactive features and leads to a significant computational speedup without any loss of accuracy.