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 …

Existing bounds on the generalization error of deep networks assume some form of smooth
or bounded dependence on the input variable, falling short of investigating the mechanisms …

Double descent presents a counter-intuitive aspect within the machine learning domain, and
researchers have observed its manifestation in various models and tasks. While some …

There has been considerable effort to better understand the generalization capabilities of
deep neural networks both as a means to unlock a theoretical understanding of their …

The prediction of tropical rain rates from atmospheric profiles poses significant challenges,
mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces …

The phenomenon of double descent has recently gained attention in supervised learning. It
challenges the conventional wisdom of the bias-variance trade-off by showcasing a …

Double descent presents a counter-intuitive aspect within the machine learning domain, and
researchers have observed its manifestation in various models and tasks. While some …

The phenomenon of model-wise double descent, where the test error peaks and then
reduces as the model size increases, is an interesting topic that has attracted the attention of …

Deep neural networks and large-scale datasets have revolutionized the field of machine
learning. However, these large networks are susceptible to overfitting to label noise …

The quality of the representations learned by neural networks depends on several factors,
including the loss function, learning algorithm, and model architecture. In this work, we use …