arXiv Machine Learning
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
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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 AI
Oct 7
Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of mainta...
arXiv Machine Learning
Oct 7
Improving Mixup Calibration with Wasserstein Distributionally Robust Optimization
arXiv:2506.17874v3 Announce Type: replace-cross Abstract: In many real-world applications, ensuring the robustness and stability of deep neural networks (DNNs) is crucial, particularly for image clas...
arXiv Machine Learning
Jun 2
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
arXiv:2503. 07325v2 Announce Type: replace Abstract: Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning.
arXiv AI
Jun 4
An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
arXiv:2606. 04409v1 Announce Type: cross Abstract: Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision.
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A Function-Centric Perspective on Flat and Sharp Minima
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