Unverified50% confidenceFactExact time

In adversarial linear logistic regression, the gap in implicit bias between adversarial training and standard training persists even when the step size is arbitrarily large.

1

Sources

50%

Confidence

Long-term

Relevance

11/6/2026

First Seen

Sources

Large-Step Adversarial Training: New Findings on Implicit Bias, Optimization, and Sharpness

Related Entities

Adversarial Linear Logistic Regressionconcept Implicit Biasconcept Adversarial Trainingconcept Standard Trainingconcept

Related Claims

UnverifiedUsing step functions or hard-threshold activation functions to simulate logic gates faces the problems of vanishing gradients or non-differentiability, making training via backpropagation difficult.74% similar UnverifiedWhen there is a pronounced heteroscedastic structure in the data, a single global early-stopping point may not be optimal, and region-adaptive training strategies are worth exploring.73% similar UnverifiedDistillation, consistency training, and adversarial objectives are the mainstream approaches to few-step generation, but they sacrifice the likelihood framework in the process of compressing the number of steps73% similar UnverifiedThe conclusions of this work currently remain at the theoretical level and are still a considerable distance from practical large-model training.72% similar

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