arXiv Machine Learning
Aug 6
Robustness Emerges Early in Training Dynamics, but Is Not Preserved
arXiv:2608. 04442v1 Announce Type: new Abstract: Robustness to natural corruptions remains a fundamental challenge for deep neural networks.
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.
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arXiv AI
Jun 16
Upper Bounds on the Generalization Error of Deep Learning Models via Local Robustness and Stability
arXiv:2606. 16883v1 Announce Type: cross Abstract: Generalization is a critical property of data-driven models, particularly deep learning models deployed in safety-critical applications.
arXiv Machine Learning
Jun 2
A unifying Bayesian framework for adversarial robustness
arXiv:2510. 09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge.
arXiv AI
Oct 1
Stochastic Gradient Descent with Momentum is Algorithmically Stable
arXiv:2605.28517v2 Announce Type: replace-cross Abstract: Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimizati...
arXiv Machine Learning
Jul 31
Generalization and Trade-off in Adversarial Training: An RKHS Perspective via Kernel Integral Operators
arXiv:2607. 27995v1 Announce Type: cross Abstract: Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications.
arXiv AI
Jun 3
Post-Hoc Robustness for Model-Based Reinforcement Learning
arXiv:2606. 03521v1 Announce Type: cross Abstract: To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations.
arXiv Statistics ML
Sep 28
Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
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 Machine Learning
Sep 14
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
The paper presents a unified framework for regularization-based robust reinforcement learning by deriving upper bounds on the performance gap between nominal and worst-case policies. These bounds are expressed as a regularization objective plus a KL-divergence penalty, explaining why KL penalties enhance robustness. The authors reformulate robust training as a constrained optimization problem, updating the Lagrange multiplier jointly with the policy to automatically tune regularization, and validate the approach with extensive adversarial evaluations on continuous control tasks.
arXiv Machine Learning
Jun 5
PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
arXiv:2606. 06293v1 Announce Type: new Abstract: Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental challenge in the adversarial setting.
Hugging Face Trending Papers
Jun 2
Post-Hoc Robustness for Model-Based Reinforcement Learning
To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Markov game.
arXiv Machine Learning
Jul 7
Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.
arXiv Machine Learning
Jul 10
Robust Bayesian Decision Making under Adversarial Uncertainty
arXiv:2607. 08590v1 Announce Type: new Abstract: Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making.