arXiv Machine Learning
Tail-Weight Control and Localized Generalization in Nearly Low-Rank Adversarial Classification
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arXiv Machine Learning
Sep 22
Statistical Inference for Adversarial Training: Central Limit Theorems via Optimal Transport
The paper rigorously analyzes the statistical and learning-theoretic properties of adversarial training models for classification, focusing on empirical optimal partial transport. It establishes two central limit theorems—one centered at the expected empirical value and another at the population value with smoothing—by leveraging the uniqueness of optimal potentials across various optimal transport formulations and empirical process theory. In the binary setting, the authors prove uniqueness of the optimal potential via a connection to multi-marginal optimal transport, and as additional results they derive stability of the saddle point, sample complexity, and concentration bounds for generalization error.
arXiv Machine Learning
Sep 2
On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
arXiv:2506.12454v2 Announce Type: replace-cross Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this wor...
arXiv Machine Learning
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How Learning Dynamics Drive Adversarially Robust Generalization?
arXiv:2410. 07719v4 Announce Type: replace Abstract: Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting.
arXiv Machine Learning
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Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
arXiv:2606. 27685v1 Announce Type: cross Abstract: Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods.
arXiv Machine Learning
Jun 2
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606. 00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments.
arXiv AI
Aug 11
Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training
arXiv:2608. 09688v1 Announce Type: cross Abstract: Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias.