Violation-based, diversity-based via k-medoids, and hybrid Benders cut filtering strategies solve more instances and cut solve times by 55-57% compared to adding all cuts.
A density-based algorithm for discovering clusters in large spatial databases with noise
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A broad empirical benchmark shows how 15 existing test selection metrics perform for fault detection, performance estimation, and retraining under corrupted, adversarial, temporal, natural, and label shifts across image, text, and Android data.
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Benders Cut Filtering for Affine Potential-Based Flow Problems with Robustness Scenarios and Topology Switching
Violation-based, diversity-based via k-medoids, and hybrid Benders cut filtering strategies solve more instances and cut solve times by 55-57% compared to adding all cuts.
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Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts
A broad empirical benchmark shows how 15 existing test selection metrics perform for fault detection, performance estimation, and retraining under corrupted, adversarial, temporal, natural, and label shifts across image, text, and Android data.