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Found 3 papers, 2 papers with code
Margin-based sampling in high dimensions: When being active is less efficient than staying passive
It is widely believed that given the same labeling budget, active learning (AL) algorithms like margin-based active learning achieve better predictive performance than passive learning (PL), albeit at a higher computational cost.
Maximizing the robust margin provably overfits on noiseless data
Numerous recent works show that overparameterization implicitly reduces variance, suggesting vanishing benefits for explicit regularization in high dimensions.
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