The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 02267v1 Announce Type: new Abstract: The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment.
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
The paper introduces a penalized distributionally robust optimization framework that allows an adversary to choose any distribution while incurring a Wasserstein penalty for deviating from the empirical distribution. It shows that the adversary’s problem can be reformulated as optimizing transport maps that push empirical samples to adversarial ones, proving that optimal maps are cyclically monotone. The authors argue that standard per-sample adversarial training violates this property and propose two remedies—multi-start particle ascent and input-convex neural network parameterization—to enforce cyclical monotonicity, demonstrating improved robustness and generalization in experiments on regression, image classification, and control tasks.
arXiv:2608. 04442v1 Announce Type: new Abstract: Robustness to natural corruptions remains a fundamental challenge for deep neural networks.
Guided Adversarial Robust Transfer (GART) learning is a new transfer learning method that relaxes the requirement for source data to closely resemble the target population. By optimizing an adversarial loss over a mixture of source distributions, GART achieves faster convergence and improved prediction performance when target data are scarce. Experiments on simulated data and on multi‑institutional biobank‑linked electronic health records for high‑density lipoprotein cholesterol demonstrate higher robustness and accuracy compared to existing transfer learning approaches.
arXiv:2512. 12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks.