Generalised Bayesian learning algorithms are increasingly popular in machine learning,
due to their PAC generalisation properties and flexibility. The present paper aims at …

Clustering is a fundamental task in unsupervised machine learning. Lloyd's 1957 algorithm
for k-means clustering remains one of the most widely used due to its speed and simplicity …

We present new excess risk bounds for general unbounded loss functions including log loss
and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold …

PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their
role is to connect the generalization ability of an aggregation distribution ρ ρ to its empirical …

In this paper, we consider the problem of linear regression with heavy-tailed distributions.
Different from previous studies that use the squared loss to measure the performance, we …

Learning with a convex loss function has been a dominating paradigm for many years. It
remains an interesting question how non-convex loss functions help improve the …

Uniform deviation bounds limit the difference between a model's expected loss and its loss
on an empirical sample uniformly for all models in a learning problem. In this paper, we …

Accurately assessing the air quality index (AQI) values and levels has become an attractive
research topic during the last decades. It is a crucial aspect when studying the possible …

We provide uniform concentration bounds on the kernel k-means clustering objective based
on Rademacher complexity by posing the underlying problem as a risk minimization task …

Bagging is a popular ensemble technique to improve the accuracy of machine learning
models. It hinges on the well-established rationale that, by repeatedly retraining on …