<ul data-eligibleForWebStory="true"><li>Randomization is being explored to boost adversarial robustness in machine learning models, with a focus on multiclass classification.</li><li>Current theoretical analysis has mainly concentrated on binary classification, leaving gaps in understanding multiclass scenarios.</li><li>A study draws from graph theory to analyze how randomization impacts adversarial risk minimization in multiclass settings.</li><li>The analysis centers on discrete data distributions, mapping adversarial risk minimization to set packing problems.</li><li>Three structural conditions on the data distribution's support are identified as crucial for randomization to enhance robustness.</li><li>Switching from deterministic to randomized solutions in certain data distributions notably decreases optimal adversarial risk.</li><li>The research underscores the significant role of randomization in fortifying multiclass classification against adversarial attacks.</li></ul>

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory

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