Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …

In machine learning, a question of great interest is understanding what examples are
challenging for a model to classify. Identifying atypical examples ensures the safe …

Byzantine-robust federated learning aims at mitigating Byzantine failures during the
federated training process, where malicious participants (known as Byzantine clients) may …

Dataset distillation extracts a small set of synthetic training samples from a large dataset with
the goal of achieving competitive performance on test data when trained on this sample. In …

This paper follows up on a recent work of Neu et al.(2021) and presents some new
information-theoretic upper bounds for the generalization error of machine learning models …

As machine learning models are increasingly employed to assist human decision-makers, it
becomes critical to communicate the uncertainty associated with these model predictions …

Supervised learning in deep neural networks is commonly performed using error
backpropagation. However, the sequential propagation of errors during the backward pass …

Large scale training requires massive parallelism to finish the training within a reasonable
amount of time. To support massive parallelism, large batch training is the key enabler but …

Most complex machine learning and modelling techniques are prone to over-fitting and may
subsequently generalise poorly to future data. Artificial neural networks are no different in …

Adaptive gradient methods, eg\textsc {Adam}, have achieved tremendous success in
machine learning. Scaling the learning rate element-wisely by a certain form of second …