A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …

We showcase important features of the dynamics of the Stochastic Gradient Descent (SGD)
in the training of neural networks. We present empirical observations that commonly used …

While there has been progress in develo** non-vacuous generalization bounds for deep
neural networks, these bounds tend to be uninformative about why deep learning works. In …

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods,
have been shown to improve a neural network's generalization performance over stochastic …

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit
regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over $2 …

We discuss methods for visualizing neural network decision boundaries and decision
regions. We use these visualizations to investigate issues related to reproducibility and …

Single-step adversarial training (AT) has received wide attention as it proved to be both
efficient and robust. However, a serious problem of catastrophic overfitting exists, ie, the …

In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives
overly expressive networks to much simpler subnetworks, thereby dramatically reducing the …

In many contexts, simpler models are preferable to more complex models and the control of
this model complexity is the goal for many methods in machine learning such as …

The success of the Adam optimizer on a wide array of architectures has made it the default
in settings where stochastic gradient descent (SGD) performs poorly. However, our …