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Benign Overfitting
Benign Overfitting
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state theorem
Benign overfitting refers to a specific classical-theory-violating phenomenon in modern ML. What is it?
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A.
Interpolating models (zero training error) can achieve near-optimal test error, contradicting the classical view that interpolating noise hurts generalization
B.
Neural networks can learn arbitrary functions, so they can always interpolate and generalize simultaneously
C.
Models that completely memorize training data have strictly better test performance than non-memorizing models
D.
Overfitting is impossible in deep learning because gradient descent regularizes implicitly, making classical analysis obsolete
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