<ul data-eligibleForWebStory="true"><li>Variable selection in high-dimensional sparse learning with group structures is challenging.</li><li>Group SLOPE is effective for adaptive selection of predictor groups but faces issues with block non-separable group effects.</li><li>Existing methods are either invalid or inefficient in handling these effects, leading to high computational costs and memory usage.</li><li>A new safe screening rule tailored for Group SLOPE efficiently identifies inactive groups with zero coefficients by addressing block non-separable group effects.</li><li>By excluding inactive groups during training, significant gains in computational efficiency and memory usage are achieved.</li><li>The screening rule can be seamlessly integrated into existing solvers for both batch and stochastic algorithms.</li><li>Theoretically, the screening rule can be safely employed with existing optimization algorithms, ensuring the same results as the original approaches.</li><li>Experimental results show that the method detects inactive feature groups effectively, enhancing computational efficiency without compromising accuracy.</li></ul>

Safe Screening Rules for Group SLOPE

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